Application of Weibull-Bayesian for the reliability analysis of distribution transformers

Ridwan, Mohd; Talib, Mohd Aizam; Ghazali, Young Zaidey Yang

doi:10.1109/peoco.2014.6814443

Cited by 3 publications

(4 citation statements)

References 9 publications

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“…Currently, the application of HI to determine the future states of transformers and the impact of different types of management strategies on the state distribution of the asset is yet to be explored. 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].…”

Section: Introductionmentioning

confidence: 99%

“…MPM is used to minimize the prediction issues related to the overreliance on simple mathematical fitting techniques [32,33]. 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].…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Introductionmentioning

confidence: 99%

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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“…Currently, there are still less studies that have been carried out to model the future condition states of transformers based on HI. Other studies, such as those in References [6,9,[15][16][17], mainly focused on the utilization of the HI to determine the future reliability of transformers and its impact on the power system network. The Markov Model (MM) is identified as one of the prediction methods that can be used to determine the future states of transformers based on HI.…”

Section: Introductionmentioning

confidence: 99%

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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“…A la fecha, los estudios de confiabilidad en los transformadores a nivel mundial se han centrado en temas puntuales como el análisis de gas disuelto en transformadores Wang(2012), (Pereira, 2012a), (Zhan, Member, Goulart, Falahi, & Rondla, 2015), entrenamiento de herramientas como redes neuronales Zhang(1996), (Kuznetsova, Li, Ruiz, & Zio, 2014), sistemas expertos con información limitada del comportamiento ciertos transformadores (Lin, Ling, & Huang, 1993), identificación variables de mantenimiento mediante lógica difusa (Arshad, Islam, & Khaliq, 2014), minería de datos para calidad de eventos (M. Guder 2014), falla de transformadores y métodos estadísticos (Soto, 2015), (Youssef, 2003), (Mkandawire, Ijumba, & Saha, 2015), (Mago, Valles, & Olaya, 2012), (Georgilakis & Kagiannas, 2014), (Ridwan & Talib, 2014), (Zompakis, Bartzas, & Soudris, 2015), (Zompakis et al, 2015), (Henao, Amaya, & Jaramillo, 2014), entre otros. La siguiente figura permite dimensionar el estudio de transformadores utilizando herramientas que permitan analizar grandes volúmenes de datos y alguna técnica inteligente para este fin.…”

Section: Introductionunclassified

Análisis De Eventos Sobre Transformadores De Distribución en Una Empresa Del Sector Eléctrico en Colombia

Guayasamin

Castrillón

Cano

2017

RCTA

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Application of Weibull-Bayesian for the reliability analysis of distribution transformers

Cited by 3 publications

References 9 publications

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

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

Estimation of Transformers Health Index Based on the Markov Chain

Análisis De Eventos Sobre Transformadores De Distribución en Una Empresa Del Sector Eléctrico en Colombia

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