Prognostics Health Management (PHM) System for Power Transformer Using Kernel Extreme Learning Machine (K-ELM)

Muhammad Abdillah, Awan Uji Krismanto, Teguh Aryo Nugroho, Herlambang Setiadi, Nita Indriani Pertiwi, Karar Mahmoud, Murman Dwi Prasetio

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Abstract

A power transformer is one of the most important and valuable components for the power system network. This device is critical to ensure power quality and reliable electricity supply for consumers. When the power transformer could not work properly or out of service in unforeseen ways, it provides a severe impact on power system utilities and customers in term of the expensive of transformer's replacement cost and revenue lost caused by the electrical blackout. To overcome these issues, the proper prognostics health management (PHM) system as a tool for condition monitoring and health assessment of these valuable assets is required. This paper proposed a PHM system based on a kernel extreme learning machine (K-ELM) for power transformer's health assessment. Two sets of variable combinations called Set-1 and Set-2 were considered to examine the robustness and efficacy of the proposed method. In Set-1, the input variables were water content, total acidity, breakdown voltage, dissipation factor, dissolved combustible gases, and 2-furfuraldehyde. While the output of PHM system was the health condition which categorized as good, moderate, and bad circumstances. Set-2 utilized water content, total acidity, breakdown voltage, dissipation factor, and interfacial tension as input variables. Whereas, the PHM system outputs consisted of four categories: normal, good, moderate, and bad. The proposed method with two sets of variables had showed the satisfactory results for transformer's health condition assessment compared to an extreme learning machine (ELM), support vector machine (SVM), and least-square support vector machine (LS-SVM) in terms of learning and testing accuracies and computation time. The proposed PHM system using the Set-1 dataset could assess the transformer health as of 100% while in terms of the testing process, the proposed PHM system has an excellent accuracy result as of 68.67%. Furthermore, the proposed PHM system using the Set-2 dataset had successfully assessed the transformer health as of 100%. In the testing phase, the proposed PHM system model has a rigorous result for its accuracy result as of 93.61%.

Original languageEnglish
Title of host publicationProceedings of the International Conference on Engineering and Information Technology for Sustainable Industry, ICONETSI 2020
PublisherACM
ISBN (Electronic)9781450387712
DOIs
Publication statusPublished - 28 Sep 2020
MoE publication typeNot Eligible
EventInternational Conference on Engineering and Information Technology for Sustainable Industry - Virtual, Online, Indonesia
Duration: 28 Sep 202029 Sep 2020

Conference

ConferenceInternational Conference on Engineering and Information Technology for Sustainable Industry
Abbreviated titleICONETSI
CountryIndonesia
Period28/09/202029/09/2020

Keywords

  • K-ELM
  • LS-SVM
  • PHMS
  • Power System Network
  • Power Transformer
  • SVM

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