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.
Highlights
Power transformers are among the most expensive and critical units in electrical distribution systems
Fuzzy logic [18], general regression neural network (GRNN) [19], neural-fuzzy (NF) [20], random forest [21], support vector machine (SVM) [22], principle component analysis (PCA), and analytical hierarchy process (AHP) [23], are among the available artificial intelligence (AI) models that have been studied in previous works of health index (HI)
The main motivation of this study is to introduce a simplified method in order to predict the HI of transformer population that is based on Statistical Distribution Model (SDM) utilizing the individual condition parameter data as a key approach to determine the HI
Summary
Power transformers are among the most expensive and critical units in electrical distribution systems. Fuzzy logic [18], general regression neural network (GRNN) [19], neural-fuzzy (NF) [20], random forest [21], support vector machine (SVM) [22], principle component analysis (PCA), and analytical hierarchy process (AHP) [23], are among the available AI models that have been studied in previous works of HI These models require extensive data to ensure a promising result in terms of prediction accuracy of the condition of the transformers. The main motivation of this study is to introduce a simplified method in order to predict the HI of transformer population that is based on Statistical Distribution Model (SDM) utilizing the individual condition parameter data as a key approach to determine the HI.
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