TNB experience in condition assessment and life management of distribution power transformers

Ghazali, Young Zaidey Yang; Talib, Mohd Aizam; Rosli, Hannah Ahmad

doi:10.1049/cp.2009.0919

Cited by 19 publications

(15 citation statements)

References 3 publications

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“…HI is defined by Reference [29] as an approach to quantify transformers condition monitoring information for asset management purposes. Nowadays, HI is adopted by most of the utilities in the world [5,7,8,10,11,30]. The conventional concept of HI formulation is based on a scoring method that is based on weighting and ranking techniques [29][30][31].…”

Section: Condition Assessment and Health Index (Hi)mentioning

confidence: 99%

“…Under CBM, a single quantitative assessment known as Health Index (HI) is normally formulated to provide the overall condition of transformers. HI normally consists of multiple input parameters such as oil condition monitoring data, loadings, design, location, and electrical/mechanical integrities [5][6][7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

“…condition monitoring data, loadings, design, location, and electrical/mechanical integrities [5][6][7][8][9][10][11]. HI provides a comprehensive condition assessment of transformers as compared to Dissolved Gases Analysis (DGA), which mainly focuses on the identification of faults [5].…”

Section: Introductionmentioning

confidence: 99%

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Estimation of Transformers Health Index Based on the Markov Chain

et al. 2017

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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%.

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Section: Condition Assessment and Health Index (Hi)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Estimation of Transformers Health Index Based on the Markov Chain

et al. 2017

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“…The asset can operate at an optimum level through prioritization of the equipment for maintenance, repair, or replacement based on condition and budget [2][3][4]. The Health Index (HI) is known as one of the key components in the CBM and has been adopted by most of the current utilities in the world [5][6][7][8][9][10]. HI provides a comprehensive assessment on transformers based on multiple parameters and has been considered as one of the important tools for diagnostic and condition monitoring techniques [11,12].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2018

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This paper presents an investigation of the condition state distribution and performance condition curve of the transformer population under different pre-determined maintenance repair rates based on the Markov Prediction Model (MPM). In total, 3195 oil samples from 373 transformers with an age between one and 25 years were tested. The previously computed Health Index (HI) prediction model of the transformer population based on MPM utilizing the nonlinear minimization technique was employed in this study. The transition probabilities for each of the states were updated based on 10%, 20% and 30% pre-determined maintenance repair rates for the sensitivity study. Next, the HI state distribution and performance condition curve were analyzed based on the Markov chain algorithm. Based on the case study, it is found that the pre-determined maintenance repair rates can affect the HI state distribution and improve the performance condition curve. The 30% pre-determined maintenance repair rate gives the highest impact, especially for the transformer population at state 4 (poor). Overall, the average percentage of change for all HI state distributions is 16.48%. A clear improvement of HI state distribution is found at state 4 (poor) where the highest percentage can be up to 63.25%.

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“…Multiple condition data are normally considered to diagnose the condition of transformers, such as oil quality, dissolved gases, furanic compounds, power factors, winding resistances, winding ratios, temperature, partial discharges, physical conditions and load tap changer conditions [2,3]. This information is embedded in a single quantitative value known as Health Index (HI), which can give overall condition status of transformers [4][5][6][7][8]. The HI can be utilized to evaluate transformer deteriorations that require diagnosis, maintenance and approaching end of life [9,10].…”

Section: Introductionmentioning

confidence: 99%

A Maintenance Cost Study of Transformers Based on Markov Model Utilizing Frequency of Transition Approach

et al. 2018

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In this paper, a maintenance cost study of transformers based on the Markov Model (MM) utilizing the Health Index (HI) is presented. In total, 120 distribution transformers of oil type (33/11 kV and 30 MVA) are examined. The HI is computed based on condition assessment data. Based on the HI, the transformers are arranged according to its corresponding states, and the transition probabilities are determined based on frequency of a transition approach utilizing the transformer transition states for the year 2013/2014 and 2012/2013. The future states of transformers are determined based on the MM chain algorithm. Finally, the maintenance costs are estimated based on future-state distribution probabilities according to the proposed maintenance policy model. The study shows that the deterioration states of the transformer population for the year 2015 can be predicted by MM based on the transformer transition states for the year 2013/2014 and 2012/2013. Analysis on the relationship between the predicted and actual computed numbers of transformers reveals that all transformer states are still within the 95% prediction interval. There is a 90% probability that the transformer population will reach State 1 after 76 years and 69 years based on the transformer transition states for the year 2013/2014 and 2012/2013. Based on the probability-state distributions, it is found that the total maintenance cost increases gradually from Ringgit Malaysia (RM) 5.94 million to RM 39.09 million based on transformer transition states for the year 2013/2014 and RM 37.56 million for the year 2012/2013 within the 20 years prediction interval, respectively.

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TNB experience in condition assessment and life management of distribution power transformers

Cited by 19 publications

References 3 publications

Estimation of Transformers Health Index Based on the Markov Chain

Estimation of Transformers Health Index Based on the Markov Chain

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

A Maintenance Cost Study of Transformers Based on Markov Model Utilizing Frequency of Transition Approach

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