Assessment of the Aging State for Transformer Oil-Barrier Insulation by Raman Spectroscopy and Optimized Support Vector Machine
Accurate assessment of the aging state of transformer oil-barrier insulation is crucial for ensuring the safe and reliable operation of power systems. This study presents the development of indoor accelerated thermal aging experiments to simulate the degradation of oil-immersed barrier insulation wi...
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2024-11-01
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| author | Deliang Liu Biao Lu Wenping Wu Wei Zhou Wansu Liu Yiye Sun Shilong Wu Guolong Shi Leiming Yuan |
| author_facet | Deliang Liu Biao Lu Wenping Wu Wei Zhou Wansu Liu Yiye Sun Shilong Wu Guolong Shi Leiming Yuan |
| author_sort | Deliang Liu |
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| description | Accurate assessment of the aging state of transformer oil-barrier insulation is crucial for ensuring the safe and reliable operation of power systems. This study presents the development of indoor accelerated thermal aging experiments to simulate the degradation of oil-immersed barrier insulation within transformers. A series of samples reflecting various aging states was obtained and categorized into six distinct groups. Raman spectroscopy analytical technology was employed to characterize the information indicative of different aging states of the oil-immersed barrier insulation. The raw Raman spectra were processed using asymmetric reweighted penalty least squares to correct baseline shifts, Savitzky–Golay (S-G) smoothing to eliminate fluctuation noise, and principal component analysis (PCA) to reduce data dimensionality by extracting principal components. A support vector machine (SVM) classifier was developed to discriminate between the Raman spectra and category labels. The SVM parameters were optimized using grid search, particle swarm optimization (PSO), and genetic algorithm (GA), yielding the optimal parameters (C and gamma). Notably, the grid search method demonstrated high efficiency in identifying the best combination of SVM parameters (<b><i>c</i></b> and <b><i>g</i></b>). Comparative analyses with varying numbers of principal components in SVM classifiers revealed that incorporating an optimal subset of PCA features achieved the highest classification accuracy of 94.44% for external validation samples, with only eight samples being misclassified into adjacent categories. This study offers technical support and a theoretical foundation for the effective assessment of the aging state of oil-barrier type insulation in transformers, contributing to the advancement of condition monitoring and maintenance strategies in power systems. |
| format | Article |
| id | doaj-art-0e7798176123481fb87f7f0d8c2b261e |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-11-01 |
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| spelling | doaj-art-0e7798176123481fb87f7f0d8c2b261e2024-12-13T16:31:41ZengMDPI AGSensors1424-82202024-11-012423748510.3390/s24237485Assessment of the Aging State for Transformer Oil-Barrier Insulation by Raman Spectroscopy and Optimized Support Vector MachineDeliang Liu0Biao Lu1Wenping Wu2Wei Zhou3Wansu Liu4Yiye Sun5Shilong Wu6Guolong Shi7Leiming Yuan8School of Information and Engineering, Suzhou University, Suzhou 234000, ChinaSchool of Information and Engineering, Suzhou University, Suzhou 234000, ChinaSchool of Information and Engineering, Suzhou University, Suzhou 234000, ChinaSchool of Information and Engineering, Suzhou University, Suzhou 234000, ChinaSchool of Information and Engineering, Suzhou University, Suzhou 234000, ChinaCollege of Electrical and Electronic Engineering, Wenzhou University, Wenzhou 325035, ChinaSuzhou Vocational and Technical College, Suzhou 234000, ChinaSchool of Information and Computer, Anhui Agricultural University, Hefei 230036, ChinaCollege of Electrical and Electronic Engineering, Wenzhou University, Wenzhou 325035, ChinaAccurate assessment of the aging state of transformer oil-barrier insulation is crucial for ensuring the safe and reliable operation of power systems. This study presents the development of indoor accelerated thermal aging experiments to simulate the degradation of oil-immersed barrier insulation within transformers. A series of samples reflecting various aging states was obtained and categorized into six distinct groups. Raman spectroscopy analytical technology was employed to characterize the information indicative of different aging states of the oil-immersed barrier insulation. The raw Raman spectra were processed using asymmetric reweighted penalty least squares to correct baseline shifts, Savitzky–Golay (S-G) smoothing to eliminate fluctuation noise, and principal component analysis (PCA) to reduce data dimensionality by extracting principal components. A support vector machine (SVM) classifier was developed to discriminate between the Raman spectra and category labels. The SVM parameters were optimized using grid search, particle swarm optimization (PSO), and genetic algorithm (GA), yielding the optimal parameters (C and gamma). Notably, the grid search method demonstrated high efficiency in identifying the best combination of SVM parameters (<b><i>c</i></b> and <b><i>g</i></b>). Comparative analyses with varying numbers of principal components in SVM classifiers revealed that incorporating an optimal subset of PCA features achieved the highest classification accuracy of 94.44% for external validation samples, with only eight samples being misclassified into adjacent categories. This study offers technical support and a theoretical foundation for the effective assessment of the aging state of oil-barrier type insulation in transformers, contributing to the advancement of condition monitoring and maintenance strategies in power systems.https://www.mdpi.com/1424-8220/24/23/7485Raman spectroscopyoil-barrier insulationaging state assessmentbaseline correctionsupport vector machine |
| spellingShingle | Deliang Liu Biao Lu Wenping Wu Wei Zhou Wansu Liu Yiye Sun Shilong Wu Guolong Shi Leiming Yuan Assessment of the Aging State for Transformer Oil-Barrier Insulation by Raman Spectroscopy and Optimized Support Vector Machine Sensors Raman spectroscopy oil-barrier insulation aging state assessment baseline correction support vector machine |
| title | Assessment of the Aging State for Transformer Oil-Barrier Insulation by Raman Spectroscopy and Optimized Support Vector Machine |
| title_full | Assessment of the Aging State for Transformer Oil-Barrier Insulation by Raman Spectroscopy and Optimized Support Vector Machine |
| title_fullStr | Assessment of the Aging State for Transformer Oil-Barrier Insulation by Raman Spectroscopy and Optimized Support Vector Machine |
| title_full_unstemmed | Assessment of the Aging State for Transformer Oil-Barrier Insulation by Raman Spectroscopy and Optimized Support Vector Machine |
| title_short | Assessment of the Aging State for Transformer Oil-Barrier Insulation by Raman Spectroscopy and Optimized Support Vector Machine |
| title_sort | assessment of the aging state for transformer oil barrier insulation by raman spectroscopy and optimized support vector machine |
| topic | Raman spectroscopy oil-barrier insulation aging state assessment baseline correction support vector machine |
| url | https://www.mdpi.com/1424-8220/24/23/7485 |
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