Application of statistical distribution models to predict health index for condition-based management of transformers

Azis, Norhafiz and Mohd Selva, Amran and Shariffudin, Nor Shafiqin and Yahaya, Muhammad Sharil and Ab Kadir, Mohd Zainal Abidin and Jasni, Jasronita and Talib, Mohd Aizam (2021) Application of statistical distribution models to predict health index for condition-based management of transformers. Applied Sciences (Switzerland), 11 (6). pp. 1-20. ISSN 2076-3417

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APPLICATION OF STATISTICAL DISTRIBUTION MODELS TO PREDICT HEALTH INDEX FOR CONDITION-BASED MANAGEMENT OF TRANSFORMERS.PDF
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Official URL: https://www.mdpi.com/2076-3417/11/6/2728

Abstract

In this study, statistical distribution model (SDM) is used to predict the health index (HI) of transformers by utilizing the condition parameters data from dissolved gas analysis (DGA), oil quality analysis (OQA), and furanic compound analysis (FCA), respectively. First, the individual condition parameters data were categorized based on transformer age from year 1 to 15. Next, the individual condition parameters data for every age were fitted while using a probability plot to find the representative distribution models. The distribution parameters were calculated based on 95% confidence level and extrapolated from year 16 to 25 through representative fitting models. The individual condition parameters data within the period were later calculated based on the estimated distribution parameters through the inverse cumulative distribution function (ICDF) of the selected distribution models. The predicted HI was then determined based on the conventional scoring method. The Chi-square test for statistical hypothesis reveals that the predicted HI for the transformer data is quite close to the calculated HI. The average percentage of absolute error is 2.7%. The HI that is predicted based on SDM yields 97.83% accuracy for the transformer data.

Item Type:	Article
Uncontrolled Keywords:	Condition-based management, Cumulative distribution function, Health index, Maximum likelihood estimate, Probability density function, Statistical distribution model
Divisions:	Faculty of Electrical and Electronic Engineering Technology
Depositing User:	Sabariah Ismail
Date Deposited:	30 May 2022 10:21
Last Modified:	30 May 2022 10:21
URI:	http://eprints.utem.edu.my/id/eprint/25944
Statistic Details:	View Download Statistic

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