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

Health Index (HI) is a common tool used for Condition-Based Maintenance (CBM) purpose. It integrates all condition parameter data using a single quantitative index to represent current transformer overall health status. This approach is useful to evaluate the long-term deter...

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Main Author:	Mohd Selva, Amran
Format:	Thesis
Language:	English
Published:	2020
Subjects:	Mathematical statistics Distribution (Probability theory) Health status indicators
Online Access:	http://psasir.upm.edu.my/id/eprint/85599/1/FK%202020%2055%20-%20ir.pdf
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spelling	my-upm-ir.855992021-12-09T04:36:20Z Application of statistical distribution models to predict health index for condition-based management of transformers 2020-04 Mohd Selva, Amran Health Index (HI) is a common tool used for Condition-Based Maintenance (CBM) purpose. It integrates all condition parameter data using a single quantitative index to represent current transformer overall health status. This approach is useful to evaluate the long-term deterioration level that may not be viable to be identified by routine inspections and individual CBM techniques. Besides, it also addresses the interaction between parameter characteristics and attributes of these CBM techniques. Presently, the existing approach is normally derived from failure rate data of transformer populations which requires significant amount of failure data and modelling efforts. Besides, a complete historical database is required if CBM data is to be used to accurately model the prediction of transformers’ health condition. This is unfeasible due to poor database management and insufficient information caused either due to missing or bad quality data. Hence, this project presents a study on the application of Statistical Distribution Model (SDM) to predict transformers Health Index (HI) based on the individual condition parameter data in dissolved gas analysis (DGA), oil quality analysis (OQA) and furanic compound analysis (FCA), respectively. First, the individual condition parameter data of the transformer population were categorised based on transformer age from year 1 to 15. Next, the individual condition parameter data of the transformer population for every age were fitted into probability plot in order to find the representative distribution models. The distribution parameters for each of the condition parameter data from year 1 to 15 were computed based on 95% confidence level of the data population samples. Subsequently, the distribution parameters for each of the condition parameter data were extrapolated from year 16 to 25 through representative fitting models. The individual condition parameter data from year 16 to 25 were computed based on the estimated distribution parameters through inverse cumulative distribution function (CDF) of the selected distribution models. The future HI of the transformer population was then estimated based on conventional scoring method. The predicted HI of the transformer population was compared with the computed HI based on Chi- square test and percentage of absolute error. Finally, the maintenance costs were estimated based on the combination of HI conditions and estimated probability of failure (POF) computed from the HI results as maintenance policy model. It is found that the SDM can be used to predict transformers’ HI. The Chi-square test for goodness-of-fit reveals that the predicted HI for the transformer population obtained based on SDM agrees with the computed HI whereby the average percentage of absolute error is 2.7%. The highest percentage of difference between predicted and computed values of HI is 6.85% along the years. Meanwhile, the accuracy of the HI prediction based on SDM for the transformer population is 97.83%. The computation method based on HI and POF relationship curve introduced in this study has inevitably help to reduce in overestimation of the investment cost as compared to using direct cost translation approach from HI results of transformer population in each year, hence a realistic capital planning can be derived as part of asset management strategies. Based on the SDM, it is found that the total estimated maintenance cost has increased from RM 18.277 million to RM 120.277 million over the prediction period of 35 years. Mathematical statistics Distribution (Probability theory) Health status indicators 2020-04 Thesis http://psasir.upm.edu.my/id/eprint/85599/ http://psasir.upm.edu.my/id/eprint/85599/1/FK%202020%2055%20-%20ir.pdf text en public masters Universiti Putra Malaysia Mathematical statistics Distribution (Probability theory) Health status indicators Azis, Norhafiz
institution	Universiti Putra Malaysia
collection	PSAS Institutional Repository
language	English
advisor	Azis, Norhafiz
topic	Mathematical statistics Distribution (Probability theory) Health status indicators
spellingShingle	Mathematical statistics Distribution (Probability theory) Health status indicators Mohd Selva, Amran Application of statistical distribution models to predict health index for condition-based management of transformers
description	Health Index (HI) is a common tool used for Condition-Based Maintenance (CBM) purpose. It integrates all condition parameter data using a single quantitative index to represent current transformer overall health status. This approach is useful to evaluate the long-term deterioration level that may not be viable to be identified by routine inspections and individual CBM techniques. Besides, it also addresses the interaction between parameter characteristics and attributes of these CBM techniques. Presently, the existing approach is normally derived from failure rate data of transformer populations which requires significant amount of failure data and modelling efforts. Besides, a complete historical database is required if CBM data is to be used to accurately model the prediction of transformers’ health condition. This is unfeasible due to poor database management and insufficient information caused either due to missing or bad quality data. Hence, this project presents a study on the application of Statistical Distribution Model (SDM) to predict transformers Health Index (HI) based on the individual condition parameter data in dissolved gas analysis (DGA), oil quality analysis (OQA) and furanic compound analysis (FCA), respectively. First, the individual condition parameter data of the transformer population were categorised based on transformer age from year 1 to 15. Next, the individual condition parameter data of the transformer population for every age were fitted into probability plot in order to find the representative distribution models. The distribution parameters for each of the condition parameter data from year 1 to 15 were computed based on 95% confidence level of the data population samples. Subsequently, the distribution parameters for each of the condition parameter data were extrapolated from year 16 to 25 through representative fitting models. The individual condition parameter data from year 16 to 25 were computed based on the estimated distribution parameters through inverse cumulative distribution function (CDF) of the selected distribution models. The future HI of the transformer population was then estimated based on conventional scoring method. The predicted HI of the transformer population was compared with the computed HI based on Chi- square test and percentage of absolute error. Finally, the maintenance costs were estimated based on the combination of HI conditions and estimated probability of failure (POF) computed from the HI results as maintenance policy model. It is found that the SDM can be used to predict transformers’ HI. The Chi-square test for goodness-of-fit reveals that the predicted HI for the transformer population obtained based on SDM agrees with the computed HI whereby the average percentage of absolute error is 2.7%. The highest percentage of difference between predicted and computed values of HI is 6.85% along the years. Meanwhile, the accuracy of the HI prediction based on SDM for the transformer population is 97.83%. The computation method based on HI and POF relationship curve introduced in this study has inevitably help to reduce in overestimation of the investment cost as compared to using direct cost translation approach from HI results of transformer population in each year, hence a realistic capital planning can be derived as part of asset management strategies. Based on the SDM, it is found that the total estimated maintenance cost has increased from RM 18.277 million to RM 120.277 million over the prediction period of 35 years.
format	Thesis
qualification_level	Master's degree
author	Mohd Selva, Amran
author_facet	Mohd Selva, Amran
author_sort	Mohd Selva, Amran
title	Application of statistical distribution models to predict health index for condition-based management of transformers
title_short	Application of statistical distribution models to predict health index for condition-based management of transformers
title_full	Application of statistical distribution models to predict health index for condition-based management of transformers
title_fullStr	Application of statistical distribution models to predict health index for condition-based management of transformers
title_full_unstemmed	Application of statistical distribution models to predict health index for condition-based management of transformers
title_sort	application of statistical distribution models to predict health index for condition-based management of transformers
granting_institution	Universiti Putra Malaysia
publishDate	2020
url	http://psasir.upm.edu.my/id/eprint/85599/1/FK%202020%2055%20-%20ir.pdf
_version_	1747813566901125120

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

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