{"title":"Causality assessment for power quality stationary disturbances","authors":"A. Pavas, Camilo Garzón","doi":"10.1109/ISGTEUROPE.2014.7028978","DOIUrl":null,"url":null,"abstract":"The location of power quality disturbances origin has been widely studied but remains unsolved so far. As disturbances location cannot be established, any evaluation regarding the cause of such disturbances cannot be completely provided either. The Method of Disturbances Interaction - MDI has proven to provide a solution to quantify the contribution of each circuit connected to a point of common coupling, allowing to identify where disturbances are concentrated. This paper goes further analysing the indicators derived from MDI in order to identify the causes of stationary power quality disturbances. Causality is evaluated by means of statistical procedures typically employed in epidemiology. Based on the results a novel discussion on responsibilities assignment is presented.","PeriodicalId":299515,"journal":{"name":"IEEE PES Innovative Smart Grid Technologies, Europe","volume":"35 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE PES Innovative Smart Grid Technologies, Europe","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISGTEUROPE.2014.7028978","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5
Abstract
The location of power quality disturbances origin has been widely studied but remains unsolved so far. As disturbances location cannot be established, any evaluation regarding the cause of such disturbances cannot be completely provided either. The Method of Disturbances Interaction - MDI has proven to provide a solution to quantify the contribution of each circuit connected to a point of common coupling, allowing to identify where disturbances are concentrated. This paper goes further analysing the indicators derived from MDI in order to identify the causes of stationary power quality disturbances. Causality is evaluated by means of statistical procedures typically employed in epidemiology. Based on the results a novel discussion on responsibilities assignment is presented.
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