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

Muhammad Sharil Yahaya, Norhafiz Azis, Amran Mohd Selva, Mohd Zainal Abidin Ab Kadir, Jasronita Jasni, Mohd Hendra Hairi, Young Zaidey Yang Ghazali and Mohd Aizam Talib
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Muhammad Sharil Yahaya: Centre for Electromagnetic and Lightning Protection, Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia
Norhafiz Azis: Centre for Electromagnetic and Lightning Protection, Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia
Amran Mohd Selva: Centre for Electromagnetic and Lightning Protection, Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia
Mohd Zainal Abidin Ab Kadir: Centre for Electromagnetic and Lightning Protection, Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia
Jasronita Jasni: Centre for Electromagnetic and Lightning Protection, Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia
Mohd Hendra Hairi: Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka, 76100 Durian Tunggal, Melaka, Malaysia
Young Zaidey Yang Ghazali: Distribution Division, Tenaga Nasional Berhad, Wisma TNB, Jalan Timur, 46200 Petaling Jaya, Selangor, Malaysia
Mohd Aizam Talib: TNB Research Sdn. Bhd., No. 1, Lorong Ayer Itam, Kawasan Institut Penyelidikan, 43000 Kajang, Selangor, Malaysia

Sustainability, 2018, vol. 10, issue 10, 1-13

Abstract: 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%.

Keywords: transformers; Health Index (HI); Markov Prediction Model (MPM); transition probabilities; nonlinear minimization; pre-determined maintenance repair rate; state distribution; performance condition curve (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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