Publication:
Measuring Color Index of Transformer Oil-Enabling Single-Wavelength Spectroscopy with Artificial Neural Network-Fuzzy Logic Model

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dc.citedby5
dc.contributor.authorHasnul Hadi M.H.en_US
dc.contributor.authorKer P.J.en_US
dc.contributor.authorLee H.J.en_US
dc.contributor.authorLeong Y.S.en_US
dc.contributor.authorThiviyanathan V.A.en_US
dc.contributor.authorHannan M.A.en_US
dc.contributor.authorJamaludin M.Z.en_US
dc.contributor.authorMahdi M.A.en_US
dc.contributor.authorid57295067100en_US
dc.contributor.authorid37461740800en_US
dc.contributor.authorid57190622221en_US
dc.contributor.authorid57202929965en_US
dc.contributor.authorid57205077992en_US
dc.contributor.authorid7103014445en_US
dc.contributor.authorid57216839721en_US
dc.contributor.authorid7005348074en_US
dc.date.accessioned2025-03-03T07:43:58Z
dc.date.available2025-03-03T07:43:58Z
dc.date.issued2024
dc.description.abstractConventionally, 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.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1109/TASE.2023.3238645
dc.identifier.epage1368
dc.identifier.issue2
dc.identifier.scopus2-s2.0-85147301297
dc.identifier.spage1358
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85147301297&doi=10.1109%2fTASE.2023.3238645&partnerID=40&md5=7146fc473b04761fd08dc10f65230962
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/36694
dc.identifier.volume21
dc.pagecount10
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitleIEEE Transactions on Automation Science and Engineering
dc.subjectComputer circuits
dc.subjectElectric transformer testing
dc.subjectErrors
dc.subjectFuzzy inference
dc.subjectFuzzy neural networks
dc.subjectInsulating oil
dc.subjectMachine learning
dc.subjectMean square error
dc.subjectOil filled transformers
dc.subjectOlive oil
dc.subjectRegression analysis
dc.subjectColor index
dc.subjectFuzzy logic modeling
dc.subjectFuzzy-Logic
dc.subjectHybrid artificial neural network
dc.subjectMachine-learning
dc.subjectOil
dc.subjectOil insulations
dc.subjectOptical path lengths
dc.subjectSingle wavelength
dc.subjectTransformer
dc.subjectColor
dc.titleMeasuring Color Index of Transformer Oil-Enabling Single-Wavelength Spectroscopy with Artificial Neural Network-Fuzzy Logic Modelen_US
dc.typeArticleen_US
dspace.entity.typePublication
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