Application of Statistical Distribution Models to Predict Health Index for Condition-Based Management of Transformers

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