{"title":"利用地理数据视图实现电力控制中心可视化","authors":"T. Overbye, Esa M. Rantanen, S. Judd","doi":"10.1109/IREP.2007.4410539","DOIUrl":null,"url":null,"abstract":"The paper introduces a new technique for power system visualization known as geographic data views, or GDVs. The impetus behind the development of GDVs is to use dynamically created visualization in order to show a wider range of power system information than is possible using the existing geographically based wide-area visualizations that are becoming common in electric power control centers. With the GDV approach power system visualizations can be dynamically created by operators or engineers using power system information along with geographic information imbedded in the power system model. The paper demonstrates the approach for several sample data sets.","PeriodicalId":214545,"journal":{"name":"2007 iREP Symposium - Bulk Power System Dynamics and Control - VII. Revitalizing Operational Reliability","volume":"156 ","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"50","resultStr":"{\"title\":\"Electric power control center visualization using Geographic Data Views\",\"authors\":\"T. Overbye, Esa M. Rantanen, S. Judd\",\"doi\":\"10.1109/IREP.2007.4410539\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The paper introduces a new technique for power system visualization known as geographic data views, or GDVs. The impetus behind the development of GDVs is to use dynamically created visualization in order to show a wider range of power system information than is possible using the existing geographically based wide-area visualizations that are becoming common in electric power control centers. With the GDV approach power system visualizations can be dynamically created by operators or engineers using power system information along with geographic information imbedded in the power system model. The paper demonstrates the approach for several sample data sets.\",\"PeriodicalId\":214545,\"journal\":{\"name\":\"2007 iREP Symposium - Bulk Power System Dynamics and Control - VII. Revitalizing Operational Reliability\",\"volume\":\"156 \",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2007-12-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"50\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2007 iREP Symposium - Bulk Power System Dynamics and Control - VII. Revitalizing Operational Reliability\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IREP.2007.4410539\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2007 iREP Symposium - Bulk Power System Dynamics and Control - VII. Revitalizing Operational Reliability","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IREP.2007.4410539","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Electric power control center visualization using Geographic Data Views
The paper introduces a new technique for power system visualization known as geographic data views, or GDVs. The impetus behind the development of GDVs is to use dynamically created visualization in order to show a wider range of power system information than is possible using the existing geographically based wide-area visualizations that are becoming common in electric power control centers. With the GDV approach power system visualizations can be dynamically created by operators or engineers using power system information along with geographic information imbedded in the power system model. The paper demonstrates the approach for several sample data sets.
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