PERTANIKA JOURNAL OF SCIENCE AND TECHNOLOGY

This work examines the failure rate of the transformer population through the application of the Markov Model (MM) and Health Index (HI). Overall, the condition parameters data extracted from 3,192 oil samples were analysed in this study. The samples were from 370 transformers with the age range between 1 and 25 years. First, both HIs and failure rates of transformers were determined based on the condition parameters data of the oil samples known as Oil Quality Analysis (OQA), Dissolved Gas Analysis (DGA), Furanic Compounds Analysis (FCA) and age. A two-parameter exponential function model was applied to represent the relationship between the HI and failure rate. Once the failure rate state was obtained, the non-linear optimisation was used to determine the transition probability for each age band. Next, the future failure rate of the transformer population was computed through the MM prediction model. The goodness-of-fit test and Mean Absolute Percentage Error (MAPE) were utilised to determine the performance of the predicted failure rate. The current study reveals that the future state of the transformer population and failure rate could be predicted through MM based on updated transition probabilities. It is observed that the MAPE between predicted and computed failure rates is 7.3%.

• Agrawal, A. K., Qian, G., Kawaguchi, A., Lagace, S., Delisle, R., Kelly, B., Weykamp, P., Conway, T., & Dubin, E. (2006). Deterioration rates of typical bridge elements in New York. In Proceedings of the Structures Congress and Exposition (pp. 1-10). ASCE Library. https://doi.org/10.1061/40889(201)128

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

• Bossi, A., Dind, J. E., Frisson, J. M., Khoudiakov, U., Light, H. F., & Al., E. (1983). An international survey of failures in large power transformers in service. In Final report of the CIGRE Working Group 12.05 (pp. 20-48). Electra.

• Brown, R. E., Member, S., Frimpong, G., Member, S., & Willis, H. L. (2018). Failure rate modeling using equipment inspection data. In IEEE Power Engineering Society General Meeting, 2004 (pp. 693-700). IEEE Publishing. 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)

• Chatfield, C. (1973). Statistical inference regarding Markov chain models. Applied Statistics, 22(1), 7-20. https://doi.org/10.2307/2346299

• CIGRE WG A2.18. (2003). Life management techniques for power transformer. CIGRE Publication.

• Edirisinghe, R., Setunge, S., & Zhang, G. (2015). Markov model-based building deterioration prediction and ISO factor analysis for building management. Journal of Management in Engineering, 31(6), Article 04015009. https://doi.org/10.1061/(asce)me.1943-5479.0000359

• Emsley, A. M., Xiao, X., Heywood, R. J., & Ali, M. (2000). Degradation of cellulosic insulation in power transformers. Part 3: Effects of oxygen and water on ageing in oil. IEE Proceedings: Science, Measurement and Technology, 147(3), 115-119. https://doi.org/10.1049/ip-smt:20000021

• Hamoud, G. A. (2011). Assessment of spare transformer requirements for distribution stations. IEEE Transactions on Power Systems, 26(1), 174-180. https://doi.org/10.1109/TPWRS.2010.2046429

• Hamoud, G. A. (2012). Use of Markov models in assessing spare transformer requirements for distribution stations. IEEE Transactions on Power Systems, 27(2), 1098-1105. https://doi.org/10.1109/TPWRS.2011.2177999

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

• 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., 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/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

• Jürgensen, J. H. (2016). Condition-based failure rate modelling for individual components in the power system (Licentiate dissertation). KTH Royal Institute of Technology, Sweden.

• Jürgensen, J. H. (2018). Individual Failure Rate Modelling and Exploratory Failure Data Analysis for Power System Components (PhD dissertation). KTH Royal Institute of Technology, Sweden.

• Jürgensen, 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

• 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

• Lundgaard, L. E., Hansen, W., Linhjell, D., & Painter, T. J. (2004). Aging of oil-impregnated paper in power transformers. IEEE Transactions on Power Delivery, 19(1), 230-239. https://doi.org/10.1109/TPWRD.2003.820175

• 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 the 2008 IEEE International Symposium on Electrical Insulation (pp. 192-196). IEEE Publishing. https://doi.org/10.1109/ELINSL.2008.4570308

• Onchiri, S. (2013). Conceptual model on application of chi-square test in education and social sciences. Educational Research and Reviews, 8(15), 1231-1241. https://doi.org/10.5897/ERR11.305

• Selva, A. M., Azis, N., Yahaya, M. S., Kadir, M. Z. A. 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

• Setunge, S., & Hasan, M. S. (2011, December 16-18). Concrete bridge deterioration prediction using Markov chain approach. In Proceedings of the International Conference on Structural Engineering, Construction and Management (ICSECM) (pp. 1-14). Earl’s Regency Kandy, Sri Lanka.

• 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

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