{"title":"一种基于深度自编码器的dfig风电机组传感器故障检测方案","authors":"A. E. Bakri, S. Sefriti, I. Boumhidi","doi":"10.1109/ISCV49265.2020.9204154","DOIUrl":null,"url":null,"abstract":"The reliability of the wind turbine doubly-fed induction generator (DFIG) is of paramount concern for adequate power production. This paper investigates an effective fault detection scheme for DFIG using the deep auto-encoder (DAE) structure. The methods contain three main steps: first, the measurement of the stator currents and voltages directly presented to the DAE to capture the characteristics of the signals effectively. Second, using those features, a neural network model is used to detect faults affecting the stator immediately. Then, a binary decision logic proposed for isolation. The results confirm the method efficiency, rapidity, robustness against the occurrence of multiple faults in the presence of measurement noise and unknown inputs.","PeriodicalId":313743,"journal":{"name":"2020 International Conference on Intelligent Systems and Computer Vision (ISCV)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A sensor fault detection scheme of DFIG-based wind turbine using deep auto-encoder approach\",\"authors\":\"A. E. Bakri, S. Sefriti, I. Boumhidi\",\"doi\":\"10.1109/ISCV49265.2020.9204154\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The reliability of the wind turbine doubly-fed induction generator (DFIG) is of paramount concern for adequate power production. This paper investigates an effective fault detection scheme for DFIG using the deep auto-encoder (DAE) structure. The methods contain three main steps: first, the measurement of the stator currents and voltages directly presented to the DAE to capture the characteristics of the signals effectively. Second, using those features, a neural network model is used to detect faults affecting the stator immediately. Then, a binary decision logic proposed for isolation. The results confirm the method efficiency, rapidity, robustness against the occurrence of multiple faults in the presence of measurement noise and unknown inputs.\",\"PeriodicalId\":313743,\"journal\":{\"name\":\"2020 International Conference on Intelligent Systems and Computer Vision (ISCV)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 International Conference on Intelligent Systems and Computer Vision (ISCV)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISCV49265.2020.9204154\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 International Conference on Intelligent Systems and Computer Vision (ISCV)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISCV49265.2020.9204154","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A sensor fault detection scheme of DFIG-based wind turbine using deep auto-encoder approach
The reliability of the wind turbine doubly-fed induction generator (DFIG) is of paramount concern for adequate power production. This paper investigates an effective fault detection scheme for DFIG using the deep auto-encoder (DAE) structure. The methods contain three main steps: first, the measurement of the stator currents and voltages directly presented to the DAE to capture the characteristics of the signals effectively. Second, using those features, a neural network model is used to detect faults affecting the stator immediately. Then, a binary decision logic proposed for isolation. The results confirm the method efficiency, rapidity, robustness against the occurrence of multiple faults in the presence of measurement noise and unknown inputs.