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

Yahaya, Muhammad Sharil; Azis, Norhafiz; Selva, Amran Mohd; Kadir, Mohd Zainal Abidin Ab; Jasni, Jasronita; Kadim, Emran Jawad; Hairi, Mohd Hendra; Ghazali, Young Zaidey Yang

doi:10.3390/en11082006

Cited by 7 publications

(7 citation statements)

References 25 publications

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“…In the work of [17], probabilistic Markov chain models were used in predicting the future performance of the transformer asset based on their HI computation for a defined span of time. Transition probabilities have been derived (using a non-linear optimization technique) to be the core element of the Markov chain model, which in turn was used to predict the future HI of the transformer asset.…”

Section: Novel Methods For Hi Computationmentioning

confidence: 99%

Application of Machine Learning in Transformer Health Index Prediction

Alqudsi

El-Hag

2019

Energies

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The presented paper aims to establish a strong basis for utilizing machine learning (ML) towards the prediction of the overall insulation health condition of medium voltage distribution transformers based on their oil test results. To validate the presented approach, the ML algorithms were tested on two databases of more than 1000 medium voltage transformer oil samples of ratings in the order of tens of MVA. The oil test results were acquired from in-service transformers (during oil sampling time) of two different utility companies in the gulf region. The illustrated procedure aimed to mimic a realistic scenario of how the utility would benefit from the use of different ML tools towards understanding the insulation health index of their transformers. This objective was achieved using two procedural steps. In the first step, three different data training and testing scenarios were used with several pattern recognition tools for classifying the transformer health condition based on the full set of input test features. In the second step, the same pattern recognition tools were used along with the three training/testing scenarios for a reduced number of test features. Also, a previously developed reduced model was the basis to reduce the needed number of tests for transformer health index calculations. It was found that reducing the number of tests did not influence the accuracy of the ML prediction models, which is considered as a significant advantage in terms of transformer asset management (TAM) cost reduction. current health condition, and all related financial costs (maintenance, operation, and failure) would result in an economic risk management plan with adequate subsequent decisions.

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Section: Novel Methods For Hi Computationmentioning

confidence: 99%

Application of Machine Learning in Transformer Health Index Prediction

Alqudsi

El-Hag

2019

Energies

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“…Finally, the 10% pre-determined maintenance policy was assigned to each state to simulate the impact of the maintenance cost of the transformer. In this study, the maintenance actions, activities, and estimated cost breakdowns were obtained based on Yahaya et al (2018b).…”

Section: Maintenance Policy and Costmentioning

confidence: 99%

“…A number of studies have examined the ideal maintenance strategy for healthcare centers using the MM (Carnero & Gómez, 2017;González-Domínguez et al, 2020). Other studies conducted by Yahaya et al (2018a); Yahaya et al (2018b) have employed the updated MM with designated maintenance policy to determine the maintenance and maintenance cost analysis based on the condition state distribution of the transformer population. Currently, the utilization of failure rate based on HI to estimate the maintenance cost and preventive frequency of the transformer population is yet to be explored.…”

Section: Introductionmentioning

confidence: 99%

Transformer Population Failure Rate State Distribution, Maintenance Cost and Preventive Frequency Study Based on Markov Model

Shariffuddin¹,

Azis²,

Jasni³

et al. 2022

JST

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This work investigates the state distributions of failure rate, performance curve, maintenance cost and preventive frequency of the transformer population through the Markov Model (MM). The condition parameters data of the oil samples known as Oil Quality Analysis (OQA), Dissolved Gas Analysis (DGA), Furanic Compounds Analysis (FCA) and age were analyzed from 370 distribution transformers. This work utilized the computed failure rate prediction model of the transformer population based on MM using the nonlinear minimization technique. First, the transition probabilities for each state were adjusted based on pre-determined maintenance repair rates of 10%, 20%, and 30%. Next, the failure rate state distributions and performance curves at various states were analyzed. Finally, the maintenance costs and preventive maintenance frequency were estimated utilizing the proposed maintenance policy models and the failure rate state probabilities. The result reveals that the transition from state 2 to state 1 with a 30% pre-determined maintenance repair rate can provide an average reduction of failure rate up to 11%. Based on the failure rate state probability, an average increment of maintenance cost from RM 18.32 million to RM 251.87 million will be incurred over 30 years. In total, 85% of the transformer population must undergo maintenance every nine months to avoid reaching very poor conditions.

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“…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.…”

Section: Introductionmentioning

confidence: 99%

“…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].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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In this study, statistical distribution model (SDM) is used to predict the health index (HI) of transformers by utilizing the condition parameters data from dissolved gas analysis (DGA), oil quality analysis (OQA), and furanic compound analysis (FCA), respectively. First, the individual condition parameters data were categorized based on transformer age from year 1 to 15. Next, the individual condition parameters data for every age were fitted while using a probability plot to find the representative distribution models. The distribution parameters were calculated based on 95% confidence level and extrapolated from year 16 to 25 through representative fitting models. The individual condition parameters data within the period were later calculated based on the estimated distribution parameters through the inverse cumulative distribution function (ICDF) of the selected distribution models. The predicted HI was then determined based on the conventional scoring method. The Chi-square test for statistical hypothesis reveals that the predicted HI for the transformer data is quite close to the calculated HI. The average percentage of absolute error is 2.7%. The HI that is predicted based on SDM yields 97.83% accuracy for the transformer data.

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

Cited by 7 publications

References 25 publications

Application of Machine Learning in Transformer Health Index Prediction

Application of Machine Learning in Transformer Health Index Prediction

Transformer Population Failure Rate State Distribution, Maintenance Cost and Preventive Frequency Study Based on Markov Model

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

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