Two-Level Fault Diagnosis of SF6 Electrical Equipment Based on Big Data Analysis
With the increase of the operating time of sulphur hexafluoride (SF6) electrical equipment, the different degrees of discharge may occur inside the equipment. It makes the insulation performance of the equipment decline and will cause serious damage to the equipment. Therefore, it is of practical si...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2019-01-01
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| Series: | Big Data and Cognitive Computing |
| Subjects: | |
| Online Access: | http://www.mdpi.com/2504-2289/3/1/4 |
| _version_ | 1819160519327612928 |
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| author | Hongxia Miao Heng Zhang Minghua Chen Bensheng Qi Jiyong Li |
| author_facet | Hongxia Miao Heng Zhang Minghua Chen Bensheng Qi Jiyong Li |
| author_sort | Hongxia Miao |
| collection | DOAJ |
| description | With the increase of the operating time of sulphur hexafluoride (SF6) electrical equipment, the different degrees of discharge may occur inside the equipment. It makes the insulation performance of the equipment decline and will cause serious damage to the equipment. Therefore, it is of practical significance to diagnose fault and assess state for SF6 electrical equipment. In recent years, the frequency of monitoring data acquisition for SF6 electrical equipment has been continuously improved and the scope of collection has been continuously expanded, which makes massive data accumulated in the substation database. In order to quickly process massive SF6 electrical equipment condition monitoring data, we built a two-level fault diagnosis model for SF6 electrical equipment on the Hadoop platform. And we use the MapReduce framework to achieve the parallelization of the fault diagnosis algorithm, which further improves the speed of fault diagnosis for SF6 electrical equipment. |
| first_indexed | 2024-12-22T16:57:44Z |
| format | Article |
| id | doaj.art-568e55f1b82e488fab07b2d36940711e |
| institution | Directory Open Access Journal |
| issn | 2504-2289 |
| language | English |
| last_indexed | 2024-12-22T16:57:44Z |
| publishDate | 2019-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Big Data and Cognitive Computing |
| spelling | doaj.art-568e55f1b82e488fab07b2d36940711e2022-12-21T18:19:25ZengMDPI AGBig Data and Cognitive Computing2504-22892019-01-0131410.3390/bdcc3010004bdcc3010004Two-Level Fault Diagnosis of SF6 Electrical Equipment Based on Big Data AnalysisHongxia Miao0Heng Zhang1Minghua Chen2Bensheng Qi3Jiyong Li4College of Internet of Things Engineering, HoHai University, Changzhou 213022, ChinaCollege of Internet of Things Engineering, HoHai University, Changzhou 213022, ChinaCollege of Internet of Things Engineering, HoHai University, Changzhou 213022, ChinaCollege of Internet of Things Engineering, HoHai University, Changzhou 213022, ChinaElectrical Engineering institute, Guangxi University, Nos.100, East University Road, Nanning 530004, ChinaWith the increase of the operating time of sulphur hexafluoride (SF6) electrical equipment, the different degrees of discharge may occur inside the equipment. It makes the insulation performance of the equipment decline and will cause serious damage to the equipment. Therefore, it is of practical significance to diagnose fault and assess state for SF6 electrical equipment. In recent years, the frequency of monitoring data acquisition for SF6 electrical equipment has been continuously improved and the scope of collection has been continuously expanded, which makes massive data accumulated in the substation database. In order to quickly process massive SF6 electrical equipment condition monitoring data, we built a two-level fault diagnosis model for SF6 electrical equipment on the Hadoop platform. And we use the MapReduce framework to achieve the parallelization of the fault diagnosis algorithm, which further improves the speed of fault diagnosis for SF6 electrical equipment.http://www.mdpi.com/2504-2289/3/1/4SF6 electrical equipmentHadoopfault diagnosisparallelism |
| spellingShingle | Hongxia Miao Heng Zhang Minghua Chen Bensheng Qi Jiyong Li Two-Level Fault Diagnosis of SF6 Electrical Equipment Based on Big Data Analysis Big Data and Cognitive Computing SF6 electrical equipment Hadoop fault diagnosis parallelism |
| title | Two-Level Fault Diagnosis of SF6 Electrical Equipment Based on Big Data Analysis |
| title_full | Two-Level Fault Diagnosis of SF6 Electrical Equipment Based on Big Data Analysis |
| title_fullStr | Two-Level Fault Diagnosis of SF6 Electrical Equipment Based on Big Data Analysis |
| title_full_unstemmed | Two-Level Fault Diagnosis of SF6 Electrical Equipment Based on Big Data Analysis |
| title_short | Two-Level Fault Diagnosis of SF6 Electrical Equipment Based on Big Data Analysis |
| title_sort | two level fault diagnosis of sf6 electrical equipment based on big data analysis |
| topic | SF6 electrical equipment Hadoop fault diagnosis parallelism |
| url | http://www.mdpi.com/2504-2289/3/1/4 |
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