One Markov Model for Spare Analysis of Distribution Power Transformers

Hamoud, G.; Yiu, Cynthia

doi:10.1109/tpwrs.2015.2431820

Cited by 26 publications

(7 citation statements)

References 10 publications

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“…It is extensively employed to evaluate and predict system safety [28], and it has a considerable influence on the decision-making process for protecting a piece of equipment's health status. Markov models are incorporated as a key strategy for addressing general probabilistic issues [29] and the reliability assessment of power electronic converters [30][31][32]. The methods of solving the Markov model include the Markov process and the semi-Markov process.…”

Section: Introductionmentioning

confidence: 99%

Quasi-Resonant Converter for Electric Vehicle Charging Applications: Analysis, Design, and Markov Model Use for Reliability Estimation

Sampath,

Nallaperumal,

Hossain

2024

Energies

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This article presents a quasi-resonant converter (QRC) with multiple sources. A QRC has many benefits, such as high gain, constant current, and nominal voltage stress on MOSFET, with an up to 49% duty cycle with fewer switches. These features of the converter make it suitable for electrical vehicle (EV) off-board charging, which requires significant voltage gain. As the switch operates under soft switching condition, the converter has reduced power loss, improved efficiency, and increased reliability. To reduce grid dependency, the suggested QRC is housed with a grid and PV at the input ports. The proposed converter is modeled using mathematical equations and examined using the MATLAB platform under different operating conditions. In this work, analysis of the steady state, along with components design, estimation of the voltage and current stresses, are addressed. Further, the reliability of the QRC based on the probability of components failure is carried out using the Markov model. The hardware results are observed to validate the design, operation, efficiency, and suitability of the proposed QRC for EV off-board charging applications. A 400-watt test rig is designed to assess the performance of QRC.

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Section: Introductionmentioning

confidence: 99%

Quasi-Resonant Converter for Electric Vehicle Charging Applications: Analysis, Design, and Markov Model Use for Reliability Estimation

Sampath,

Nallaperumal,

Hossain

2024

Energies

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“…The MM has been widely applied to forecast the conditions of bridges, bridge elements, pavements, storm water piping components and steel hydraulic structures in civil engineering [16][17][18][19][20]. The MM has also been used to predict the deterioration states of switchgear populations, spare units and fault occurrence of transformers [21][22][23][24]. Based on the MM, the maintenance cost can be estimated to assist asset managers in planning their future budgetary [25][26][27].…”

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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“…It is based on a probability decision process where future decisions on maintenance schemes depend on actual assets performances [14,18,19]. MM had been widely implemented in References [14,[18][19][20][21][22][23][24][25][26][27][28]] to model the deterioration of different types of equipment. In civil engineering, MM had been applied in References [14,[18][19][20][21][22][23][24] to model the degradation of bridge deck and elements, pavement, water piping components, and steel hydraulic structures.…”

Section: Introductionmentioning

confidence: 99%

“…In civil engineering, MM had been applied in References [14,[18][19][20][21][22][23][24] to model the degradation of bridge deck and elements, pavement, water piping components, and steel hydraulic structures. MM had also been utilized in References [25][26][27][28] for electrical equipment such as modeling the condition of switchgear oils, the identification of faults, and transformers spare units. In this study, an innovative approach is proposed that utilizes MM to determine the deterioration performance curve based on computed HI from the transformer population.…”

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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One Markov Model for Spare Analysis of Distribution Power Transformers

Cited by 26 publications

References 10 publications

Quasi-Resonant Converter for Electric Vehicle Charging Applications: Analysis, Design, and Markov Model Use for Reliability Estimation

Quasi-Resonant Converter for Electric Vehicle Charging Applications: Analysis, Design, and Markov Model Use for Reliability Estimation

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

Estimation of Transformers Health Index Based on the Markov Chain

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