{"title":"变压器溶解气体数据的统计分析","authors":"Navneet Bhargava, Aparna R. Gupta, Litesh Bopche","doi":"10.1109/ICACAT.2018.8933700","DOIUrl":null,"url":null,"abstract":"The Genetic Algorithm is practical to resolve the obstacles of tiny samples and provide better prognostication for non linear behaviors and it is desirable for the Dissolved Gas Analysis in Power Transformers. The GA generates the initial accumulation at random prosper and scrutiny space faster and modifies the global search cognition and convergent speed. As question arises whether the data was nonlinear or not? It was decided to do the data analysis first. Thus the gas concentration in ppm (parts per million) of all the DGA samples was checked for non linearity.","PeriodicalId":6575,"journal":{"name":"2018 International Conference on Advanced Computation and Telecommunication (ICACAT)","volume":"11 1","pages":"1-3"},"PeriodicalIF":0.0000,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Statistical Analysis of Data for Dissolved Gases in Transformer\",\"authors\":\"Navneet Bhargava, Aparna R. Gupta, Litesh Bopche\",\"doi\":\"10.1109/ICACAT.2018.8933700\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The Genetic Algorithm is practical to resolve the obstacles of tiny samples and provide better prognostication for non linear behaviors and it is desirable for the Dissolved Gas Analysis in Power Transformers. The GA generates the initial accumulation at random prosper and scrutiny space faster and modifies the global search cognition and convergent speed. As question arises whether the data was nonlinear or not? It was decided to do the data analysis first. Thus the gas concentration in ppm (parts per million) of all the DGA samples was checked for non linearity.\",\"PeriodicalId\":6575,\"journal\":{\"name\":\"2018 International Conference on Advanced Computation and Telecommunication (ICACAT)\",\"volume\":\"11 1\",\"pages\":\"1-3\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 International Conference on Advanced Computation and Telecommunication (ICACAT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICACAT.2018.8933700\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 International Conference on Advanced Computation and Telecommunication (ICACAT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICACAT.2018.8933700","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Statistical Analysis of Data for Dissolved Gases in Transformer
The Genetic Algorithm is practical to resolve the obstacles of tiny samples and provide better prognostication for non linear behaviors and it is desirable for the Dissolved Gas Analysis in Power Transformers. The GA generates the initial accumulation at random prosper and scrutiny space faster and modifies the global search cognition and convergent speed. As question arises whether the data was nonlinear or not? It was decided to do the data analysis first. Thus the gas concentration in ppm (parts per million) of all the DGA samples was checked for non linearity.