Version 1
: Received: 31 May 2018 / Approved: 1 June 2018 / Online: 1 June 2018 (05:58:18 CEST)
How to cite:
-, S.; Prasojo, R.A. Power Transformer Insulation Assessment Based on Oil-Paper Measurement Data Using SVM-Classifier. Preprints2018, 2018060002. https://doi.org/10.20944/preprints201806.0002.v1
-, S.; Prasojo, R.A. Power Transformer Insulation Assessment Based on Oil-Paper Measurement Data Using SVM-Classifier. Preprints 2018, 2018060002. https://doi.org/10.20944/preprints201806.0002.v1
-, S.; Prasojo, R.A. Power Transformer Insulation Assessment Based on Oil-Paper Measurement Data Using SVM-Classifier. Preprints2018, 2018060002. https://doi.org/10.20944/preprints201806.0002.v1
APA Style
-, S., & Prasojo, R.A. (2018). Power Transformer Insulation Assessment Based on Oil-Paper Measurement Data Using SVM-Classifier. Preprints. https://doi.org/10.20944/preprints201806.0002.v1
Chicago/Turabian Style
-, S. and Rahman A. Prasojo. 2018 "Power Transformer Insulation Assessment Based on Oil-Paper Measurement Data Using SVM-Classifier" Preprints. https://doi.org/10.20944/preprints201806.0002.v1
Abstract
Oil immersed paper insulation condition is a crucial aspect of power transformer’s life condition diagnostic. The measurement testing database collected over the years made it possible for researchers to implement classification analysis to in-service power transformer. This article presents classification analysis of transformer oil-immersed paper insulation condition. The measurements data (dielectric characteristics, dissolved gas analysis, and furanic compounds) of 149 transformers with primary voltage of 150 kV had been gathered and analyzed. The algorithm used for developing classification model is Support Vector Machine (SVM). The model has been trained and tested using different datasets. Different models have been created and the best chosen, resulting in 90.63% accuracy in predicting the oil-immersed paper insulation condition. Further implementation was executed to classify oil-paper condition of 19 Transformers which Furan data is not available. The classification results combined, reviewed, and compared to conventional assessment methods and standards, confirming that the model developed has the ability to do classification of current oil-paper condition based on Dissolved Gasses and Dielectric Characteristics.
Keywords
support vector machine; classification analysis; power transformer condition assessment; oil immersed paper insulation; dga; dielectric characteristics, furanic compounds
Subject
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
