Estimation of Transformers Health Index Based On Condition Parameter Factor and Hidden Markov Model

Abstract

This paper presents a study to estimate future Health Index (HI) of transformer population based on Hidden Markov Model (HMM). In this paper, HI was represented as hidden state and the condition parameter factors in the HI algorithm namely Dissolved Gas Analysis Factor (DGAF), Oil Quality Analysis Factor (OQAF) and Furfural Analysis Factor (FAF) were represented as the observable states. A case study of 1130 oil samples from 373 oiltyped distribution transformers (33/11 kV and 30 MVA) were examined. First, the mean for HI in each year was computed and the transition probabilities for the condition data were obtained based on non-linear optimization technique. Next, the emission probabilities for each of the condition parameter factors were derived based on frequency of occurrence method. Subsequently, the future states probability distribution was computed based on the HMM prediction model and viterbi algorithm was applied to find the best optimal path sequence of HI for the respective observable condition. Finally, the predicted and computed HI were compared to the hypothesized distribution. Majority of the predicted HI agrees with computed HI. Predicted HI based on OQAF records the most accurate estimation throughout the sampling years. Inconsistencies are observed in year 2 and between year 7 and 10 for the predicted HI based on FAF. The predicted HI based on DGAF is in line with the computed HI during the first 2 years and deviates at the later stage of the sampling period.

High voltage power transformer condition assessment considering the health index value and its decreasing rate

“…Health Index is a comprehensive method for the transformer assessment. This method is a scientific approach to objectively and quantitatively assess power transformer because it combines the results of operating data observations, field inspections and laboratory testing [20][21][22][23]. This method calculates all those parameters to produce a health index value from equipment.…”

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

“…Equating Equation ( 15) to zero, the MLE (μ ,σ) of (µ, σ) solutions are expressed in Equation (17).μ = X (17)…”

“…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.…”

“…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].…”

“…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]. MM and HMM both do not rely heavily on historical condition parameters data.…”