Fault Diagnosis of Power Transformers With Membership Degree
Power transformers are important equipment for power systems, and a dissolved gas analysis (DGA) is widely used to detect incipient faults in oil-pregnant transformers. The conventional methods are prone to misinterpreting the gas data near the boundaries and the correct rate is low. Though a high c...
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| Format: | Article |
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IEEE
2019-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8654646/ |
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| author | Enwen Li Linong Wang Bin Song |
| author_facet | Enwen Li Linong Wang Bin Song |
| author_sort | Enwen Li |
| collection | DOAJ |
| description | Power transformers are important equipment for power systems, and a dissolved gas analysis (DGA) is widely used to detect incipient faults in oil-pregnant transformers. The conventional methods are prone to misinterpreting the gas data near the boundaries and the correct rate is low. Though a high correct rate is reported with intelligent methods as artificial neural network, support vector machine, and so on, these methods are usually too complicated to be implemented practically on a wide range. Based on clustering techniques, this paper proposes a new method for fault diagnosis of transformers with the DGA. A reference fault set is provided, and the fault diagnosis is implemented by calculating the membership of the DGA data to the reference fault set. Test with credible DGA dataset (201 field cases) shows that the correct rate of the new method is 89%, while the David triangle method is 79% and the IEC ratio method is 59%, which demonstrate the superiority of the proposed method to the conventional ones. The new method is simple and highly accurate, indicating a good application prospect in engineering practice. |
| format | Article |
| id | doaj-art-8c73b5aa854c456faa314530aefdcf35 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-8c73b5aa854c456faa314530aefdcf352025-01-16T00:00:59ZengIEEEIEEE Access2169-35362019-01-017287912879810.1109/ACCESS.2019.29022998654646Fault Diagnosis of Power Transformers With Membership DegreeEnwen Li0https://orcid.org/0000-0002-2249-8140Linong Wang1Bin Song2School of Electrical Engineering and Automation, Wuhan University, Wuhan, ChinaSchool of Electrical Engineering and Automation, Wuhan University, Wuhan, ChinaSchool of Electrical Engineering and Automation, Wuhan University, Wuhan, ChinaPower transformers are important equipment for power systems, and a dissolved gas analysis (DGA) is widely used to detect incipient faults in oil-pregnant transformers. The conventional methods are prone to misinterpreting the gas data near the boundaries and the correct rate is low. Though a high correct rate is reported with intelligent methods as artificial neural network, support vector machine, and so on, these methods are usually too complicated to be implemented practically on a wide range. Based on clustering techniques, this paper proposes a new method for fault diagnosis of transformers with the DGA. A reference fault set is provided, and the fault diagnosis is implemented by calculating the membership of the DGA data to the reference fault set. Test with credible DGA dataset (201 field cases) shows that the correct rate of the new method is 89%, while the David triangle method is 79% and the IEC ratio method is 59%, which demonstrate the superiority of the proposed method to the conventional ones. The new method is simple and highly accurate, indicating a good application prospect in engineering practice.https://ieeexplore.ieee.org/document/8654646/Power transformerfuzzy clusteringfault diagnosismembership degree |
| spellingShingle | Enwen Li Linong Wang Bin Song Fault Diagnosis of Power Transformers With Membership Degree IEEE Access Power transformer fuzzy clustering fault diagnosis membership degree |
| title | Fault Diagnosis of Power Transformers With Membership Degree |
| title_full | Fault Diagnosis of Power Transformers With Membership Degree |
| title_fullStr | Fault Diagnosis of Power Transformers With Membership Degree |
| title_full_unstemmed | Fault Diagnosis of Power Transformers With Membership Degree |
| title_short | Fault Diagnosis of Power Transformers With Membership Degree |
| title_sort | fault diagnosis of power transformers with membership degree |
| topic | Power transformer fuzzy clustering fault diagnosis membership degree |
| url | https://ieeexplore.ieee.org/document/8654646/ |
| work_keys_str_mv | AT enwenli faultdiagnosisofpowertransformerswithmembershipdegree AT linongwang faultdiagnosisofpowertransformerswithmembershipdegree AT binsong faultdiagnosisofpowertransformerswithmembershipdegree |