Measuring Color Index of Transformer Oil-Enabling Single-Wavelength Spectroscopy with Artificial Neural Network-Fuzzy Logic Model
Conventionally, the color index of transformer oil is determined by a color comparator based on the American Society for Testing and Materials (ASTM) D 1500 standard. The equipment requires humans to operate, which leads to human error and limited number of samples tested per day. This work proposes...
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2025
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| author | Hasnul Hadi M.H. Ker P.J. Lee H.J. Leong Y.S. Thiviyanathan V.A. Hannan M.A. Jamaludin M.Z. Mahdi M.A. |
| author2 | 57295067100 |
| author_facet | 57295067100 Hasnul Hadi M.H. Ker P.J. Lee H.J. Leong Y.S. Thiviyanathan V.A. Hannan M.A. Jamaludin M.Z. Mahdi M.A. |
| author_sort | Hasnul Hadi M.H. |
| building | UNITEN Library |
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| content_provider | Universiti Tenaga Nasional |
| content_source | UNITEN Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | Conventionally, the color index of transformer oil is determined by a color comparator based on the American Society for Testing and Materials (ASTM) D 1500 standard. The equipment requires humans to operate, which leads to human error and limited number of samples tested per day. This work proposes the utilization of single-wavelength spectroscopy with 405 nm laser diode using artificial neural network (ANN) to determine the color index of transformer oil. Two ANN models were developed using data collected from 50 oil samples with different optical pathlengths of 1 to 10 mm, and laser output powers of 1 to 15 mW. The first model classified the input into different color indices and another model correlated the input parameters through regression analysis to determine the color index. A hybrid ANN-fuzzy logic model was also developed to improve the color index prediction. The root-mean-squared error (RMSE) obtained from the prediction by ANN regressor and ANN classifier are 0.5602 and 0.6416, respectively. The hybrid ANN-fuzzy logic model improves the RMSE especially for optical pathlengths < 5 mm, which is required for measuring samples with high color index. This proposed method reduces the dependency on complex optoelectronic hardware to obtain highly accurate results.Note to Practitioners - Unlike the conventional testing method for color index of transformer oil that requires human observation, the findings of this study enables the possibility of compact and smart portable device through the utilization of single wavelength spectroscopy with machine learning models. With no human involvement, more computational power with lesser hardware dependency, the maintenance cost and error can be reduced. This proposed method can potentially be applied to measure the color of other amber-colored liquid products such as olive oil, honey and others. ? 2004-2012 IEEE. |
| format | Article |
| id | my.uniten.dspace-36694 |
| institution | Universiti Tenaga Nasional |
| publishDate | 2025 |
| publisher | Institute of Electrical and Electronics Engineers Inc. |
| record_format | dspace |
| spelling | my.uniten.dspace-366942025-03-03T15:43:58Z Measuring Color Index of Transformer Oil-Enabling Single-Wavelength Spectroscopy with Artificial Neural Network-Fuzzy Logic Model Hasnul Hadi M.H. Ker P.J. Lee H.J. Leong Y.S. Thiviyanathan V.A. Hannan M.A. Jamaludin M.Z. Mahdi M.A. 57295067100 37461740800 57190622221 57202929965 57205077992 7103014445 57216839721 7005348074 Computer circuits Electric transformer testing Errors Fuzzy inference Fuzzy neural networks Insulating oil Machine learning Mean square error Oil filled transformers Olive oil Regression analysis Color index Fuzzy logic modeling Fuzzy-Logic Hybrid artificial neural network Machine-learning Oil Oil insulations Optical path lengths Single wavelength Transformer Color Conventionally, the color index of transformer oil is determined by a color comparator based on the American Society for Testing and Materials (ASTM) D 1500 standard. The equipment requires humans to operate, which leads to human error and limited number of samples tested per day. This work proposes the utilization of single-wavelength spectroscopy with 405 nm laser diode using artificial neural network (ANN) to determine the color index of transformer oil. Two ANN models were developed using data collected from 50 oil samples with different optical pathlengths of 1 to 10 mm, and laser output powers of 1 to 15 mW. The first model classified the input into different color indices and another model correlated the input parameters through regression analysis to determine the color index. A hybrid ANN-fuzzy logic model was also developed to improve the color index prediction. The root-mean-squared error (RMSE) obtained from the prediction by ANN regressor and ANN classifier are 0.5602 and 0.6416, respectively. The hybrid ANN-fuzzy logic model improves the RMSE especially for optical pathlengths < 5 mm, which is required for measuring samples with high color index. This proposed method reduces the dependency on complex optoelectronic hardware to obtain highly accurate results.Note to Practitioners - Unlike the conventional testing method for color index of transformer oil that requires human observation, the findings of this study enables the possibility of compact and smart portable device through the utilization of single wavelength spectroscopy with machine learning models. With no human involvement, more computational power with lesser hardware dependency, the maintenance cost and error can be reduced. This proposed method can potentially be applied to measure the color of other amber-colored liquid products such as olive oil, honey and others. ? 2004-2012 IEEE. Final 2025-03-03T07:43:58Z 2025-03-03T07:43:58Z 2024 Article 10.1109/TASE.2023.3238645 2-s2.0-85147301297 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85147301297&doi=10.1109%2fTASE.2023.3238645&partnerID=40&md5=7146fc473b04761fd08dc10f65230962 https://irepository.uniten.edu.my/handle/123456789/36694 21 2 1358 1368 Institute of Electrical and Electronics Engineers Inc. Scopus |
| spellingShingle | Computer circuits Electric transformer testing Errors Fuzzy inference Fuzzy neural networks Insulating oil Machine learning Mean square error Oil filled transformers Olive oil Regression analysis Color index Fuzzy logic modeling Fuzzy-Logic Hybrid artificial neural network Machine-learning Oil Oil insulations Optical path lengths Single wavelength Transformer Color Hasnul Hadi M.H. Ker P.J. Lee H.J. Leong Y.S. Thiviyanathan V.A. Hannan M.A. Jamaludin M.Z. Mahdi M.A. Measuring Color Index of Transformer Oil-Enabling Single-Wavelength Spectroscopy with Artificial Neural Network-Fuzzy Logic Model |
| title | Measuring Color Index of Transformer Oil-Enabling Single-Wavelength Spectroscopy with Artificial Neural Network-Fuzzy Logic Model |
| title_full | Measuring Color Index of Transformer Oil-Enabling Single-Wavelength Spectroscopy with Artificial Neural Network-Fuzzy Logic Model |
| title_fullStr | Measuring Color Index of Transformer Oil-Enabling Single-Wavelength Spectroscopy with Artificial Neural Network-Fuzzy Logic Model |
| title_full_unstemmed | Measuring Color Index of Transformer Oil-Enabling Single-Wavelength Spectroscopy with Artificial Neural Network-Fuzzy Logic Model |
| title_short | Measuring Color Index of Transformer Oil-Enabling Single-Wavelength Spectroscopy with Artificial Neural Network-Fuzzy Logic Model |
| title_sort | measuring color index of transformer oil-enabling single-wavelength spectroscopy with artificial neural network-fuzzy logic model |
| topic | Computer circuits Electric transformer testing Errors Fuzzy inference Fuzzy neural networks Insulating oil Machine learning Mean square error Oil filled transformers Olive oil Regression analysis Color index Fuzzy logic modeling Fuzzy-Logic Hybrid artificial neural network Machine-learning Oil Oil insulations Optical path lengths Single wavelength Transformer Color |
| url_provider | http://dspace.uniten.edu.my/ |