Rule - based Fault Diagnosis Expert System for Wind Turbine
Under the trend of increasing installed capacity of wind power, the intelligent fault diagnosis of wind turbine is of great significance to the safe and efficient operation of wind farms. Based on the knowledge of fault diagnosis of wind turbines, this paper builds expert system diagnostic knowledge...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2017-01-01
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| Series: | ITM Web of Conferences |
| Online Access: | https://doi.org/10.1051/itmconf/20171107005 |
| _version_ | 1819162717364158464 |
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| author | Deng Xiao-Wen Gao Qing-Shui Zhang Chu Hu Di Yang Tao |
| author_facet | Deng Xiao-Wen Gao Qing-Shui Zhang Chu Hu Di Yang Tao |
| author_sort | Deng Xiao-Wen |
| collection | DOAJ |
| description | Under the trend of increasing installed capacity of wind power, the intelligent fault diagnosis of wind turbine is of great significance to the safe and efficient operation of wind farms. Based on the knowledge of fault diagnosis of wind turbines, this paper builds expert system diagnostic knowledge base by using confidence production rules and expert system self-learning method. In Visual Studio 2013 platform, C # language is selected and ADO.NET technology is used to access the database. Development of Fault Diagnosis Expert System for Wind Turbine. The purpose of this paper is to realize on-line diagnosis of wind turbine fault through human-computer interaction, and to improve the diagnostic capability of the system through the continuous improvement of the knowledge base. |
| first_indexed | 2024-12-22T17:32:40Z |
| format | Article |
| id | doaj.art-7c20842f70bd418a989cf385dae3c964 |
| institution | Directory Open Access Journal |
| issn | 2271-2097 |
| language | English |
| last_indexed | 2024-12-22T17:32:40Z |
| publishDate | 2017-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | ITM Web of Conferences |
| spelling | doaj.art-7c20842f70bd418a989cf385dae3c9642022-12-21T18:18:35ZengEDP SciencesITM Web of Conferences2271-20972017-01-01110700510.1051/itmconf/20171107005itmconf_ist2017_07005Rule - based Fault Diagnosis Expert System for Wind TurbineDeng Xiao-Wen0Gao Qing-Shui1Zhang Chu2Hu Di3Yang Tao4Electric Power Research Institute of Guangdong Power Grid Co., Ltd.Electric Power Research Institute of Guangdong Power Grid Co., Ltd.Electric Power Research Institute of Guangdong Power Grid Co., Ltd.School of Energy and Power Engineering, Huazhong University of Science & TechnologySchool of Energy and Power Engineering, Huazhong University of Science & TechnologyUnder the trend of increasing installed capacity of wind power, the intelligent fault diagnosis of wind turbine is of great significance to the safe and efficient operation of wind farms. Based on the knowledge of fault diagnosis of wind turbines, this paper builds expert system diagnostic knowledge base by using confidence production rules and expert system self-learning method. In Visual Studio 2013 platform, C # language is selected and ADO.NET technology is used to access the database. Development of Fault Diagnosis Expert System for Wind Turbine. The purpose of this paper is to realize on-line diagnosis of wind turbine fault through human-computer interaction, and to improve the diagnostic capability of the system through the continuous improvement of the knowledge base.https://doi.org/10.1051/itmconf/20171107005 |
| spellingShingle | Deng Xiao-Wen Gao Qing-Shui Zhang Chu Hu Di Yang Tao Rule - based Fault Diagnosis Expert System for Wind Turbine ITM Web of Conferences |
| title | Rule - based Fault Diagnosis Expert System for Wind Turbine |
| title_full | Rule - based Fault Diagnosis Expert System for Wind Turbine |
| title_fullStr | Rule - based Fault Diagnosis Expert System for Wind Turbine |
| title_full_unstemmed | Rule - based Fault Diagnosis Expert System for Wind Turbine |
| title_short | Rule - based Fault Diagnosis Expert System for Wind Turbine |
| title_sort | rule based fault diagnosis expert system for wind turbine |
| url | https://doi.org/10.1051/itmconf/20171107005 |
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