{"title":"铁磁共振的检测与鉴定","authors":"Heba Abu Sharbain, A. Osman, A. El-Hag","doi":"10.1109/ICMSAO.2017.7934904","DOIUrl":null,"url":null,"abstract":"This paper presents an artificial intelligent based method to detect ferroresonance. The proposed detection method utilizes wavelet transform combined with artificial neural network to detect ferroresonance. Using this method, ferroresonance can be identified and differentiated. The results show that the proposed procedure is effective in identifying ferroresonance from other transients such as capacitor switching. Moreover, they indicate that the used neural network model has an acceptable precision in the recognition of ferroresonance and by adjusting the right parameters, the highest precision is achieved.","PeriodicalId":265345,"journal":{"name":"2017 7th International Conference on Modeling, Simulation, and Applied Optimization (ICMSAO)","volume":"126 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"Detection and identification of ferroresonance\",\"authors\":\"Heba Abu Sharbain, A. Osman, A. El-Hag\",\"doi\":\"10.1109/ICMSAO.2017.7934904\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents an artificial intelligent based method to detect ferroresonance. The proposed detection method utilizes wavelet transform combined with artificial neural network to detect ferroresonance. Using this method, ferroresonance can be identified and differentiated. The results show that the proposed procedure is effective in identifying ferroresonance from other transients such as capacitor switching. Moreover, they indicate that the used neural network model has an acceptable precision in the recognition of ferroresonance and by adjusting the right parameters, the highest precision is achieved.\",\"PeriodicalId\":265345,\"journal\":{\"name\":\"2017 7th International Conference on Modeling, Simulation, and Applied Optimization (ICMSAO)\",\"volume\":\"126 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-04-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 7th International Conference on Modeling, Simulation, and Applied Optimization (ICMSAO)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICMSAO.2017.7934904\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 7th International Conference on Modeling, Simulation, and Applied Optimization (ICMSAO)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICMSAO.2017.7934904","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
This paper presents an artificial intelligent based method to detect ferroresonance. The proposed detection method utilizes wavelet transform combined with artificial neural network to detect ferroresonance. Using this method, ferroresonance can be identified and differentiated. The results show that the proposed procedure is effective in identifying ferroresonance from other transients such as capacitor switching. Moreover, they indicate that the used neural network model has an acceptable precision in the recognition of ferroresonance and by adjusting the right parameters, the highest precision is achieved.