Fault prediction for power transformer using optical spectrum of transformer oil and data mining analysis
Cost effectiveness; Data mining; Electric discharges; Fault detection; Forecasting; Oil tanks; Optical correlation; Optical materials; Power transformers; Spectrum analysis; Voltage control; Data analytic tools; Dissolved gas analysis; Electrical discharges; On- load tap changers; Optical characteri...
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Institute of Electrical and Electronics Engineers Inc.
2023
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my.uniten.dspace-256792023-05-29T16:12:38Z Fault prediction for power transformer using optical spectrum of transformer oil and data mining analysis Fauzi N.A. Ali N.H.N. Ker P.J. Thiviyanathan V.A. Leong Y.S. Sabry A.H. Jamaludin M.D.Z.B. Lo C.K. Mun L.H. 57205073909 57196922007 37461740800 57205077992 57202929965 56602511900 57216839721 36721595300 6507460925 Cost effectiveness; Data mining; Electric discharges; Fault detection; Forecasting; Oil tanks; Optical correlation; Optical materials; Power transformers; Spectrum analysis; Voltage control; Data analytic tools; Dissolved gas analysis; Electrical discharges; On- load tap changers; Optical characteristics; Optical spectroscopy; Optical spectroscopy techniques; Periodic preventive maintenance; Oil filled transformers Periodic preventive maintenance of power transformer should be conducted for its health monitoring and early fault detection. Transformer oil is a vital element where its contents and properties need to be monitored during the service life of a power transformer. This paper presents an optical spectroscopy measurement from 200 nm to 3300 nm to characterize the transformer oil, which were sampled from the main tanks and 'on-load tap changer' of power transformers. The correlation of the optical characteristics in the range of 2120 nm to 2220 nm to the Dissolved Gas Analysis results and Duval Triangle interpretation demonstrates that the low energy electrical discharges, high energy electrical discharges as well as the thermal faults rated at temperatures above 700�C in power transformers can be accurately predicted. For faster and accurate analysis of fault prediction, a data mining analytics tool was constructed using Rapid Miner server to analyze and verify the predictions for a total of 108 oil samples. For the optimization, continuous iterations were performed to determine the best absorbance-wavelength combination that can improve the accuracy of the prediction. The performance of the optical spectroscopy technique integrated with data analytic tool was analyzed and it was found that the technique contributes to a high accuracy of 98.1% in fault prediction. It is a cost-effective and quicker complementing approach to carry out pre-screening of the transformer oil in order to know the condition of the power transformers based on the transformer oil's optical characteristics. � 2013 IEEE. Final 2023-05-29T08:12:38Z 2023-05-29T08:12:38Z 2020 Article 10.1109/ACCESS.2020.3011504 2-s2.0-85090389019 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090389019&doi=10.1109%2fACCESS.2020.3011504&partnerID=40&md5=355313318a182f4a9a2bc63fa20bd7b6 https://irepository.uniten.edu.my/handle/123456789/25679 8 9146611 136374 136381 All Open Access, Gold Institute of Electrical and Electronics Engineers Inc. Scopus |
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Cost effectiveness; Data mining; Electric discharges; Fault detection; Forecasting; Oil tanks; Optical correlation; Optical materials; Power transformers; Spectrum analysis; Voltage control; Data analytic tools; Dissolved gas analysis; Electrical discharges; On- load tap changers; Optical characteristics; Optical spectroscopy; Optical spectroscopy techniques; Periodic preventive maintenance; Oil filled transformers |
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57205073909 |
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57205073909 Fauzi N.A. Ali N.H.N. Ker P.J. Thiviyanathan V.A. Leong Y.S. Sabry A.H. Jamaludin M.D.Z.B. Lo C.K. Mun L.H. |
| format |
Article |
| author |
Fauzi N.A. Ali N.H.N. Ker P.J. Thiviyanathan V.A. Leong Y.S. Sabry A.H. Jamaludin M.D.Z.B. Lo C.K. Mun L.H. |
| spellingShingle |
Fauzi N.A. Ali N.H.N. Ker P.J. Thiviyanathan V.A. Leong Y.S. Sabry A.H. Jamaludin M.D.Z.B. Lo C.K. Mun L.H. Fault prediction for power transformer using optical spectrum of transformer oil and data mining analysis |
| author_sort |
Fauzi N.A. |
| title |
Fault prediction for power transformer using optical spectrum of transformer oil and data mining analysis |
| title_short |
Fault prediction for power transformer using optical spectrum of transformer oil and data mining analysis |
| title_full |
Fault prediction for power transformer using optical spectrum of transformer oil and data mining analysis |
| title_fullStr |
Fault prediction for power transformer using optical spectrum of transformer oil and data mining analysis |
| title_full_unstemmed |
Fault prediction for power transformer using optical spectrum of transformer oil and data mining analysis |
| title_sort |
fault prediction for power transformer using optical spectrum of transformer oil and data mining analysis |
| publisher |
Institute of Electrical and Electronics Engineers Inc. |
| publishDate |
2023 |
| _version_ |
1806426568209203200 |
| score |
13.214268 |