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Iranian Journal of Chemistry and Chemical Engineering، جلد ۴۴، شماره ۵، صفحات ۱۴۵۸-۱۴۸۱
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| عنوان فارسی |
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| چکیده فارسی مقاله |
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| کلیدواژه‌های فارسی مقاله |
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| عنوان انگلیسی |
Enhanced Power Transformer Fault Diagnosis Using Key Chemical Gases with DGA, Integrating Machine Learning and Traditional Methods |
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| چکیده انگلیسی مقاله |
The reliability of fault diagnosis and the stability of electrical grids demand accurate analysis of key chemical gases in power transformer oil. DGA, which quantitatively measures concentrations of critical chemical gases, is still a cornerstone technique in the field, but is infamous for its interpretative complexity: the correlation between gas levels and specific fault types is too complex. The proposed methodology in this study attempts to integrate the strengths of traditional methods of DGA interpretation along with the power of a machine learning model, specifically a Random Forest algorithm. The process comprises the preprocessing of DGA data to extract meaningful chemical features from them further developing the model using machine learning to classify the different kinds of faults based upon those chemical features. This approach has been validated on multiple scenarios for the data coming from DGA transformer faults after a lot of testing. Results show that this method delivered an average accuracy of 95.86% for three types of faults and 93.67% for the same types of faults with varying conditions. For six types of faults, the delivery was placed at an average accuracy and consistency of 88.85% and 87.47%, respectively. This approach significantly shows improved performance in the traditional methods of diagnostics while promising much more accurate fault detection. In addition to enhanced diagnostic accuracy, it supports proactive, hence preventive, maintenance strategies, resulting in improved system efficiency and reduced downtime. The paper details a technique that combines chemical data analysis with machine learning, from which distinct solutions can be conceived to address the complex challenges facing industries. |
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| کلیدواژه‌های انگلیسی مقاله |
Machine Learning,Transformers,Fault diagnosis,Dissolved gas analysis,Key Chemical Gases |
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| نویسندگان مقاله |
Lalitha Sangeetham Dharuman |
R.M.K. Engineering College, Kavaraipettai 601 206, INDIA
Anitha Gunasekaran |
B.S. Abdur Rahman Crescent Institute of Science and Technology, Chennai 600048, INDIA
Parimala Vellivel |
KPR Institute of Engineering and Technology, Coimbatore 641 407, INDIA
Muthukrishnan Athi Narayanasamy |
Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi 600 062, INDIA
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| نشانی اینترنتی |
https://ijcce.ac.ir/article_722946_2abb97336f86eca4fbfaefb23d290e77.pdf |
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| زبان مقاله منتشر شده |
en |
| موضوعات مقاله منتشر شده |
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