Estimation of Transformers Health Index Based on the Markov Chain

Abstract

This paper presents a study on the application of the Markov Model (MM) to determine the transformer population states based on Health Index (HI). In total, 3195 oil samples from 373 transformers ranging in age from 1 to 25 years were analyzed. First, the HI of transformers was computed based on yearly individual oil condition monitoring data that consisted of oil quality, dissolved gases, and furanic compounds. Next, the average HI for each age was computed and the transition probabilities were obtained based on a nonlinear optimization technique. Finally, the future deterioration performance curve of the transformers was determined based on the MM chain algorithm. It was found that the MM can be used to predict the future transformers condition states. The chi-squared goodness-of-fit analysis revealed that the predicted HI for the transformer population obtained based on MM agrees with the average computed HI along the years, and the average error is 3.59%.

Effect of Pre-Determined Maintenance Repair Rates on the Health Index State Distribution and Performance Condition Curve Based on the Markov Prediction Model for Sustainable Transformers Asset Management Strategies

“…MPM is a dynamic stochastic probabilistic approach that is capable of predicting the future condition state of transformers [20,42]. MPM has been widely applied to model the deterioration of different types of equipment [21][22][23][24][25][26][27][28].…”

“…The second assumption is that the transformer undergoes normal ageing and remains in one state for each time interval. The assumptions were translated into the transition probabilities, given as P ij [20][21][22][23]. P ij is the probability of equipment decaying from state i to j in a specific time interval.…”

“…The aim of this paper is to investigate the effect of pre-determined maintenance repair rates by MPM on the HI state distribution and performance condition curve of the transformer population. The previous oil condition monitoring data from 373 distribution transformers with ratings of 33 kV and 30 MVA are tested [20]. The current study is a continuation of a previous study in [20], whereby the state distribution and performance condition curve of the transformer population with and without pre-determined maintenance repair rates are compared and analyzed.…”

“…Most of the studies on the prediction of transformers' future states are based on the paper ageing model and statistical failure analyses [13][14][15][16][17][18][19]. There are only a few studies that have been carried out to estimate the transformers' future states based on MPM [20]. The studies on the future state prediction of asset based on MPM have been mainly carried out in civil engineering [21][22][23][24][25][26].…”

“…In addition, the dependency on the failure data for the computation is also minimized for MPM [17][18][19]34]. The application of MPM based on transformer population condition data was considered an innovative approach to predict the future deterioration states [20]. MPM can also be used to examine the effect of maintenance, repair, and replacement on the state distribution of the asset [26,29,31].…”