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

• Main Authors: Yahaya, Muhammad Sharil, Azis, Norhafiz, Mohd Selva, Amran, Ab Kadir, Mohd Zainal Abidin, Jasni, Jasronita, Hairi, Mohd Hendra, Yang Ghazali, Young Zaidey, Talib, Mohd Aizam
• Format: Article
• Language: English
• Published: MDPI 2018
• Online Access: http://psasir.upm.edu.my/id/eprint/15371/1/15371.pdf; http://psasir.upm.edu.my/id/eprint/15371/; https://www.mdpi.com/2071-1050/10/10/3399

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%.