Application of Markov Model to Estimate Individual Condition Parameters for Transformers

Abstract

This paper presents a study to estimate individual condition parameters of the transformer population based on Markov Model (MM). The condition parameters under study were hydrogen (H2), methane (CH4), acetylene (C2H2), ethylene (C2H4), ethane (C2H6), carbon monoxide (CO), carbon dioxide (CO2), dielectric breakdown voltage, interfacial tension, colour, acidity, water content, and 2-furfuraldehyde (2-FAL). First, the individual condition parameter of the transformer population was ranked and sorted based on recommended limits as per IEEE Std. C57. 104-2008 and IEEE Std. C57.106-2015. Next, the mean for each of the condition parameters was computed and the transition probabilities for each condition parameters were obtained based on non-linear optimization technique. Next, the future states probability distribution was computed based on the MM prediction model. Chi-square test and percentage of absolute error analysis were carried out to find the goodness-of-fit between predicted and computed condition parameters. It is found that estimation for majority of the individual condition parameter of the transformer population can be carried out by MM. The Chi-square test reveals that apart from CH4 and C2H4, the condition parameters are outside the rejection region that indicates agreement between predicted and computed values. It is also observed that the lowest and highest percentages of differences between predicted and computed values of all the condition parameters are 81.46% and 98.52%, respectively.

On Approaching Relevant Cost-Effective Sustainable Maintenance of Mineral Oil-Filled Electrical Transformers

“…The main outcome of applying SDM was establishing a modelling framework for future health index of transformers with even limited historical CM-parameters data. MPM is also applied in an investigation [20] based on CM-parameters to model the future deterioration in the transformers. However, all the mentioned models provided relevant information about the overall current condition status of transformers, and predicted the condition in the future, but none of them integrated a method to detect early faults.…”

“…Assessment of the transformer's condition, based on CM parameters, is a crucial key in the CBM. In this context, several mathematical models are utilized in modelling the deterioration status in order to assess the overall condition of transformers [11,19,20]. The drawbacks of these models are modelling the deterioration with a single path without considering the complexity of the deterioration in transformers [21], and the assessment of overall condition without providing information about the root cause of the fault [20].…”

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

“…Statistical approaches that are based on Markov Model (MM) [15,16] and Hidden Markov Model (HMM) [17] have been used to predict the condition states of transformer population. Previous work in [15] utilizes the transition probabilities of the transformer's condition states that are derived from HI for a specific year interval.…”

“…Previous work in [15] utilizes the transition probabilities of the transformer's condition states that are derived from HI for a specific year interval. The other study in [16] implements a similar approach, except that the transformer's condition states are derived from condition parameters data. A previous study in [17] utilizes HMM to predict the transformer's condition states in a different approach as compared to MM [15,16].…”

“…The other study in [16] implements a similar approach, except that the transformer's condition states are derived from condition parameters data. A previous study in [17] utilizes HMM to predict the transformer's condition states in a different approach as compared to MM [15,16]. Hidden state transition and emission probabilities derived from condition parameters data have been computed to predict the HI of transformers [17].…”