{"title":"选取真实的有轨电车变电所进行过载分析,采用人工神经网络进行结构优化","authors":"M. Dudzik, A. Jagiełło, S. Drapik, J. Prusak","doi":"10.1109/SPEEDAM.2018.8445340","DOIUrl":null,"url":null,"abstract":"The paper constitutes a continuation of research on load variability of rectifier units. The research are made for the selected tram substation. The performed analysis uses the actual measurements. This time the analysis focuses on relation between the maximum loads and 60 minutes overloads currents. The second part of the paper shows the effectiveness of use of the feedforward type artificial neural network. The effectiveness of the analyze was calculated for 250 times, for 50 cases. The results shown in the paper were obtained for optimal structure of the artificial neural network. The results presented in this publication prove to be the best results among the results known by the authors of the work.","PeriodicalId":117883,"journal":{"name":"2018 International Symposium on Power Electronics, Electrical Drives, Automation and Motion (SPEEDAM)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":"{\"title\":\"The selected real tramway substation overload analysis using the optimal structure of an artificial neural network\",\"authors\":\"M. Dudzik, A. Jagiełło, S. Drapik, J. Prusak\",\"doi\":\"10.1109/SPEEDAM.2018.8445340\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The paper constitutes a continuation of research on load variability of rectifier units. The research are made for the selected tram substation. The performed analysis uses the actual measurements. This time the analysis focuses on relation between the maximum loads and 60 minutes overloads currents. The second part of the paper shows the effectiveness of use of the feedforward type artificial neural network. The effectiveness of the analyze was calculated for 250 times, for 50 cases. The results shown in the paper were obtained for optimal structure of the artificial neural network. The results presented in this publication prove to be the best results among the results known by the authors of the work.\",\"PeriodicalId\":117883,\"journal\":{\"name\":\"2018 International Symposium on Power Electronics, Electrical Drives, Automation and Motion (SPEEDAM)\",\"volume\":\"7 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"10\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 International Symposium on Power Electronics, Electrical Drives, Automation and Motion (SPEEDAM)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SPEEDAM.2018.8445340\",\"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 Symposium on Power Electronics, Electrical Drives, Automation and Motion (SPEEDAM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SPEEDAM.2018.8445340","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The selected real tramway substation overload analysis using the optimal structure of an artificial neural network
The paper constitutes a continuation of research on load variability of rectifier units. The research are made for the selected tram substation. The performed analysis uses the actual measurements. This time the analysis focuses on relation between the maximum loads and 60 minutes overloads currents. The second part of the paper shows the effectiveness of use of the feedforward type artificial neural network. The effectiveness of the analyze was calculated for 250 times, for 50 cases. The results shown in the paper were obtained for optimal structure of the artificial neural network. The results presented in this publication prove to be the best results among the results known by the authors of the work.