Power Transformer Insulation System Health Index with Missing Data Prediction using Random Forest

Geby Chintia; Rahman Azis Prasojo; Suwarno

doi:10.1109/ICPEA56918.2023.10093216

Power Transformer Insulation System Health Index with Missing Data Prediction using Random Forest

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Geby Chintia, Rahman Azis Prasojo, Suwarno

2023 2023 IEEE 3rd International Conference in Power Engineering Applications: Shaping Sustainability Through Power Engineering Innovation, ICPEA 2023 Conference paper Cited by 6

Abstract

Health Index approach is currently one of the most common ways to assess the overall condition of power transformers. Data unavailability is still a problem in Health Index assessment. This paper discusses the prediction of transformer health conditions using five missing data replacement methods, which are removed parameter, average value, assume good, SLR, and Random Forest prediction. Seven scenarios based were simulated based on three missing parameters, namely 2FAL, IFT and Water Content. The accuracy is evaluated using the Health Index calculated with complete parameter. As much as 504 units of 150 kV power transformers were used in the analysis. The results show that Random Forest method produced the highest accuracy rate among the other methods with average value of 92%. © 2023 IEEE.

Affiliations

Institut Teknologi Bandung, School of Electrical Engineering and Informatics, Bandung, Indonesia; Politeknik Negeri Malang, Electrical Engineering Department, Malang, Indonesia

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