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

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%.

On Approaching Relevant Cost-Effective Sustainable Maintenance of Mineral Oil-Filled Electrical Transformers

“…There are few studies related to the use of mathematical theories to improve the effectiveness of maintenance strategy. An investigation in the study [11] applied Markov prediction model (MPM) to assess transformer condition based on available CM parameters under various pre-determined maintenance repair rates, and to ascertain the corrective action for maintenance, repair, and replacement. The MPM provides a simulation of the asset's deterioration using health index % (HI) to assess the condition as good, fair, poor, etc.…”

“…The MPM provides a simulation of the asset's deterioration using health index % (HI) to assess the condition as good, fair, poor, etc. HI is defined in [11,19] as a method that uses historical condition data in a single measurable index in order to provide a comprehensive assessment of the current state of transformers. In another study [19], a statistical distribution model (SDM) was utilized to predict the HI of transformers using the data of CM-parameters.…”

“…The common regular maintenance strategy used for high voltage substation power transformers is condition-based maintenance (CBM) [3,11] which is defined according to EN 13306:2017 [12] as "preventive maintenance, which includes assessment of physical conditions, analysis and the possible ensuing maintenance actions". Oil analysis technology is commonly used in the condition monitoring (CM) parameters to detect faults, such as partial discharge, arcing, oxidation, and corrosion [2,[13][14][15].…”

“…Assessment of the transformer's condition, based on CM parameters, is a crucial key in the CBM. In this context, several mathematical models are utilized in modelling the deterioration status in order to assess the overall condition of transformers [11,19,20]. The drawbacks of these models are modelling the deterioration with a single path without considering the complexity of the deterioration in transformers [21], and the assessment of overall condition without providing information about the root cause of the fault [20].…”

“…Sustainable maintenance of transformers is considered a new challenge that requires an understanding of the process of sustainable development and the integration between social and environmental aspects [11,22,23]. The vital objectives of sustainable maintenance are extending the lifetime of the transformers and reducing the negative impacts in a way that contributes to the organization's own competitiveness [24].…”