Application of statistical distribution models to predict health index for condition-based management of transformers
In this study, statistical distribution model (SDM) is used to predict the health index (HI) of transformers by utilizing the condition parameters data from dissolved gas analysis (DGA), oil quality analysis (OQA), and furanic compound analysis (FCA), respectively. First, the individual condition pa...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Published: |
Multidisciplinary Digital Publishing Institute
2021
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| Online Access: | http://psasir.upm.edu.my/id/eprint/95960/ |
| _version_ | 1848862263734173696 |
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| author | Mohd Selva, Amran Azis, Norhafiz Shariffudin, Nor Shafiqin Ab Kadir, Mohd Zainal Abidin Jasni, Jasronita Yahaya, Muhammad Sharil Talib, Mohd Aizam |
| author_facet | Mohd Selva, Amran Azis, Norhafiz Shariffudin, Nor Shafiqin Ab Kadir, Mohd Zainal Abidin Jasni, Jasronita Yahaya, Muhammad Sharil Talib, Mohd Aizam |
| author_sort | Mohd Selva, Amran |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | In this study, statistical distribution model (SDM) is used to predict the health index (HI) of transformers by utilizing the condition parameters data from dissolved gas analysis (DGA), oil quality analysis (OQA), and furanic compound analysis (FCA), respectively. First, the individual condition parameters data were categorized based on transformer age from year 1 to 15. Next, the individual condition parameters data for every age were fitted while using a probability plot to find the representative distribution models. The distribution parameters were calculated based on 95% confidence level and extrapolated from year 16 to 25 through representative fitting models. The individual condition parameters data within the period were later calculated based on the estimated distribution parameters through the inverse cumulative distribution function (ICDF) of the selected distribution models. The predicted HI was then determined based on the conventional scoring method. The Chi-square test for statistical hypothesis reveals that the predicted HI for the transformer data is quite close to the calculated HI. The average percentage of absolute error is 2.7%. The HI that is predicted based on SDM yields 97.83% accuracy for the transformer data. |
| first_indexed | 2025-11-15T13:14:15Z |
| format | Article |
| id | upm-95960 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-15T13:14:15Z |
| publishDate | 2021 |
| publisher | Multidisciplinary Digital Publishing Institute |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-959602023-03-16T02:37:20Z http://psasir.upm.edu.my/id/eprint/95960/ Application of statistical distribution models to predict health index for condition-based management of transformers Mohd Selva, Amran Azis, Norhafiz Shariffudin, Nor Shafiqin Ab Kadir, Mohd Zainal Abidin Jasni, Jasronita Yahaya, Muhammad Sharil Talib, Mohd Aizam In this study, statistical distribution model (SDM) is used to predict the health index (HI) of transformers by utilizing the condition parameters data from dissolved gas analysis (DGA), oil quality analysis (OQA), and furanic compound analysis (FCA), respectively. First, the individual condition parameters data were categorized based on transformer age from year 1 to 15. Next, the individual condition parameters data for every age were fitted while using a probability plot to find the representative distribution models. The distribution parameters were calculated based on 95% confidence level and extrapolated from year 16 to 25 through representative fitting models. The individual condition parameters data within the period were later calculated based on the estimated distribution parameters through the inverse cumulative distribution function (ICDF) of the selected distribution models. The predicted HI was then determined based on the conventional scoring method. The Chi-square test for statistical hypothesis reveals that the predicted HI for the transformer data is quite close to the calculated HI. The average percentage of absolute error is 2.7%. The HI that is predicted based on SDM yields 97.83% accuracy for the transformer data. Multidisciplinary Digital Publishing Institute 2021 Article PeerReviewed Mohd Selva, Amran and Azis, Norhafiz and Shariffudin, Nor Shafiqin and Ab Kadir, Mohd Zainal Abidin and Jasni, Jasronita and Yahaya, Muhammad Sharil and Talib, Mohd Aizam (2021) Application of statistical distribution models to predict health index for condition-based management of transformers. Applied Sciences-Basel, 11 (6). art. no. 2728. pp. 1-20. ISSN 2076-3417 https://www.mdpi.com/2076-3417/11/6/2728 10.3390/app11062728 |
| spellingShingle | Mohd Selva, Amran Azis, Norhafiz Shariffudin, Nor Shafiqin Ab Kadir, Mohd Zainal Abidin Jasni, Jasronita Yahaya, Muhammad Sharil Talib, Mohd Aizam Application of statistical distribution models to predict health index for condition-based management of transformers |
| title | Application of statistical distribution models to predict health index for condition-based management of transformers |
| title_full | Application of statistical distribution models to predict health index for condition-based management of transformers |
| title_fullStr | Application of statistical distribution models to predict health index for condition-based management of transformers |
| title_full_unstemmed | Application of statistical distribution models to predict health index for condition-based management of transformers |
| title_short | Application of statistical distribution models to predict health index for condition-based management of transformers |
| title_sort | application of statistical distribution models to predict health index for condition-based management of transformers |
| url | http://psasir.upm.edu.my/id/eprint/95960/ http://psasir.upm.edu.my/id/eprint/95960/ http://psasir.upm.edu.my/id/eprint/95960/ |