PERTANIKA JOURNAL OF SOCIAL SCIENCES AND HUMANITIES

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.

• Borovkov, K. (2003). Markov chains. In Elements of Stochastic Modelling (pp. 75-128). World Scientific. https://doi.org/10.1142/9789812779199_0003

• Brown, R. E., Member, S., Frimpong, G., Member, S., & Willis, H. L. (2018). Failure rate modeling using equipment inspection data. IEEE Transactions on Power Systems, 19(2), 782-787. https://doi.org/10.1109/TPWRS.2004.825824

• Camahan, J. V., Davis, W. J., Shahin, M. Y., Keane, P. L., & Wu, M. I. (1987). Optimal maintenance decisions for pavement management. Journal of Transportation Engineering, 113(5), 554-572. https://doi.org/10.1061/(ASCE)0733-947X(1987)113:5(554)

• Carnero, M. C., & Gómez, A. (2017). Maintenance strategy selection in electric power distribution systems. Energy, 129, 255-272. https://doi.org/10.1016/j.energy.2017.04.100

• Cesare, M. A., & Al, E. (1992). Modelling bridge deterioration using Markov Chains. Journal of Transportation Engineering, 118(6), 820-833. https://doi.org/10.1061/(ASCE)0733-947X(1992)118

• Ghazali, Y. Z. Y., Talib, M. A., & Soosai, A. M. (2015, June 15-18). TNB approach on managing asset retirement for distribution transformers. In 23rd International Conference on Electricity Distribution (pp. 1-5). Lyon, France.

• González-Domínguez, J., Sánchez-Barroso, G., & García-Sanz-Calcedo, J. (2020). Scheduling of preventive maintenance in healthcare buildings using Markov chain. Applied Sciences, 10(15), Article 5263. https://doi.org/10.3390/APP10155263

• Hamoud, G. A., & Yiu, C. (2020). Assessment of spare parts for system components using a Markov model. IEEE Transactions on Power Systems, 35(4), 3114-3121. https://doi.org/10.1109/TPWRS.2020.2966913

• Hoskins, R. P., Strbac, G., & Brint, A. T. (1999). Modelling the degradation of condition indices. IEEE Proceedings: Generation, Transmission and Distribution, 146(4), 386-392. https://doi.org/10.1049/ip-gtd:19990063

• Islam, M. M., Lee, G., & Hettiwatte, S. N. (2017). Application of a general regression neural network for health index calculation of power transformers. International Journal of Electrical Power and Energy Systems, 93, 308-315. https://doi.org/10.1016/j.ijepes.2017.06.008

• Jahromi, A., Piercy, R., Cress, S., Service, J., & Fan, W. (2009). An approach to power transformer asset management using health index. IEEE Electrical Insulation Magazine, 25(2), 20-34. https://doi.org/10.1109/MEI.2009.4802595

• Jürgensen, J. H. (2016). Condition-based failure rate modelling for individual components in the power system (Licentiate dissertation). KTH Royal Institute of Technology, Sweden. https://www.diva-portal.org/smash/record.jsf?pid=diva2%3A931186&dswid=1085

• Jürgensen, J. H. (2018). Individual failure rate modelling and exploratory failure data analysis for power system components (Ph.D. dissertation). KTH Royal Institute of Technology, Sweden. https://www.diva-portal.org/smash/record.jsf?pid=diva2%3A1251662&dswid=-8289

• Jurgensen, J. H., Brodersson, A. L., Nordstrom, L., & Hilber, P. (2018). Impact assessment of remote control and preventive maintenance on the failure rate of a disconnector population. IEEE Transactions on Power Delivery, 33(4), 1501-1509. https://doi.org/10.1109/TPWRD.2017.2710482

• Jürgensen, J. H., Nordstrom, L., & Hilber, P. (2016a). A review and discussion of failure rate heterogeneity in power system reliability assessment. In 2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS) (pp. 1-8). IEEE Publishing. https://doi.org/https://doi.org/10.1109/PMAPS.2016.7764078

• Jürgensen, J. H., Nordström, L., & Hilber, P. (2016b). Individual failure rates for transformers within a population based on diagnostic measures. Electric Power Systems Research, 141, 354-362. https://doi.org/10.1016/j.epsr.2016.08.015

• Li, L., Li, F., Chen, Z., & Sun, L. (2016). Use of Markov chain model based on actual repair status to predict bridge deterioration in Shanghai, China. Transportation Research Record, 2550, 106-114. https://doi.org/10.3141/2550-14

• Lindquist, T. M., Bertling, L., & Eriksson, R. (2005). Estimation of disconnector contact condition for modelling the effect of maintenance and ageing. In 2005 IEEE Russia Power Tech (pp. 1-7). IEEE Publishing. https://doi.org/10.1109/PTC.2005.4524406

• McDonald, D. (2004). Elements of applied probability for engineering, mathematics and systems science. World Scientific. https://doi.org/10.1142/5456

• Micevski, T., Kuczera, G., & Coombes, P. (2002). Markov model for storm water pipe deterioration. Journal of Infrastructure Systems, 8(2), 49-56. https://doi.org/10.1061/(asce)1076-0342(2002)8:2(49)

• Naderian, A., Cress, S., Piercy, R., Wang, F., & Service, J. (2008). An approach to determine the health index of power transformers. In Conference Record of IEEE International Symposium on Electrical Insulation (pp. 192-196). IEEE Publishing. https://doi.org/10.1109/ELINSL.2008.4570308

• Riveros, G. A., & Arredondo, E. (2010). Guide to the development of a deterioration rate curve using condition state inspection data. US Army Corps of Engineering. https://apps.dtic.mil/sti/pdfs/ADA527894.pdf

• Selva, A. M., Azis, N., Yahaya, M. S., Ab Kadir, M. Z. A., Jasni, J., Ghazali, Y. Z. Y., & Talib, M. A. (2018). Application of markov model to estimate individual condition parameters for transformers. Energies, 11(8), Article 2114. https://doi.org/10.3390/en11082114

• Shariffuddin, N. S., Azis, N., Selva, A. M., Yahaya, M. S., Jasni, J., & Talib, M. A. (2021a). Investigation on the relationship between failure rates and health index of distribution transformer population. In Proceedings of the IEEE International Conference on Properties and Applications of Dielectric Materials (pp. 119-122). IEEE Publishing. https://doi.org/10.1109/ICPADM49635.2021.9493977

• Shariffuddin, N. S., Azis, N., Selva, A. M., Yahaya, M. S., Jasni, J., Kadir, M. Z. A. A., & Talib, A. M. (2021b). Failure Rate Estimation for Transformer Population based on Health Index through Markov Model Approach. 29(4), 3029-3042. https://doi.org/10.47836/pjst.29.4.42

• Stevens, N. A., Lydon, M., Marshall, A. H., & Taylor, S. (2020). Identification of bridge key performance indicators using survival analysis for future network-wide structural health monitoring. Sensors (Switzerland), 20(23), 1-15. https://doi.org/10.3390/s20236894

• Tenbohlen, S., Vahidi, F., Gebauer, J., & Gmbh, M. R. (2011, August 22-26). Assessment of power transformer reliability. In 17th International Symposium on High Voltage Engineering (pp. 1-6). Hannover, Germany.

• White, J. R., & Widup, R. (2014). Factors to consider when determining maintenance intervals. IEEE Transactions on Industry Applications, 50(1), 188-194. https://doi.org/10.1109/TIA.2013.2288229

• Yahaya, M. S., Azis, N., Kadir, M. Z. A. A., Jasni, J., Hairi, M. H., & Talib, M. A. (2017). Estimation of transformers health index based on the markov chain. Energies, 10(11), 1-11. https://doi.org/10.3390/en10111824

• Yahaya, M. S., Azis, N., Selva, A. M., Kadir, M. Z. A. A., Jasni, J., Hairi, M. H., Ghazali, Y. Z. Y., & Talib, M. A. (2018a). 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. Sustainability (Switzerland), 10(10), Article 3399. https://doi.org/10.3390/su10103399

• Yahaya, M. S., Azis, N., Selva, A. M., Kadir, M. Z. A. A., Jasni, J., Kadim, E. J., Hairi, M. H., & Ghazali, Y. Z. Y. (2018b). A maintenance cost study of transformers based on markov model utilizing frequency of transition approach. Energies, 11(8), Article 2006. https://doi.org/10.3390/en11082006