PERTANIKA JOURNAL OF SCIENCE AND TECHNOLOGY

This study evaluates the Loss-of-Life (LOL) based on the modified relative aging rate of an Oil Natural Air Natural (ONAN) transformer with voltage and power ratings of 132/33 kV and 60 MVA. The study’s methodology included the determination of the Hotspot Temperature (HST) based on the differential equation in IEC 60076-7. The loading and ambient temperature profiles for HST determination are forecasted based on the Seasonal Autoregressive Integrated Moving Average (SARIMA). Next, a new relative aging rate was developed based on the Arrhenius equation, considering the pre-exponential factors governed by oxygen, moisture in paper, and acids at different content levels. The LOL was computed based on the new relative aging rate. The study’s main aim is to examine the impact of pre-exponential factors on the LOL based on modified Arrhenius and relative aging rate. The results indicate that the LOLs for different conditions increase as the oxygen, moisture, low molecular weight acid (LMA), and high molecular weight acid (HMA) increase. The LOLs are 46 days, 1,354 days, and 2,662 days in the presence of 12,000 ppm, 21,000 ppm, and 30,000 ppm of oxygen. In 1%, 3%, and 5% moisture, the LOLs are 477 days, 2,799 days, and 7,315 days. At 1% moisture, the LOL is 1,418 days for LMA, while for HMA, it is 122 days. The LMA has the highest impact on the LOL compared to other aging acceleration factors.

• Afzali, M., Afzali, A., & Zahedi, G. (2011). Ambient air temperature forecasting using artificial neural network approach. In 2011 International Conference on Environmental and Computer Science IPCBEE (Vol.19, pp. 176-180). IACSIT Press.

• Agrawal, R. K., Muchahary, F., & Tripathi, M. M. (2018). Long term load forecasting with hourly predictions based on long-short-term-memory networks. In 2018 IEEE Texas Power and Energy Conference (TPEC) (pp. 1-6). IEEE Publishing. https://doi.org/10.1109/TPEC.2018.8312088

• Al-Shaikh, H., Rahman, M. A., & Zubair, A. (2019). Short-term electric demand forecasting for power systems using similar months approach based SARIMA. In 2019 IEEE International Conference on Power, Electrical, and Electronics and Industrial Applications (PEEIACON) (pp. 122-126). IEEE Publishing. https://doi.org/10.1109/PEEIACON48840.2019.9071939

• Arshad, M., & Islam, S. M. (2011). Significance of cellulose power transformer condition assessment. IEEE Transactions on Dielectrics and Electrical Insulation, 18(5), 1591-1598. https://doi.org/10.1109/TDEI.2011.6032829

• Arshad, M., Islam, S. M., & Khaliq, A. (2004). Power transformer aging and life extension. In 8th International Conference on Probabilistic Methods Applied to Power Systems (pp. 498-501). IEEE Publishing.

• Biçen, Y., Çilliyüz Y., Aras, F. & Aydugan, G. (2012). Aging of paper insulation in natural ester & mineral oil. Electrical and Electronic Engineering, 2(3), 141-146. https://doi.org/10.5923/j.eee.20120203.06

• Biçen, Y., Çilliyüz, Y., Aras, F., & Aydugan, G. (2011). An assessment on aging model of IEEE/IEC standards for natural and mineral oil-immersed transformer. In 2011 IEEE International Conference on Dielectric Liquids (pp 1-4). IEEE Publishing. https://doi.org/10.1109/ICDL.2011.6015442

• Kumar, B. L. P., & Mathew, R. (2016). Asset management of transformer based on loss of life calculation. In 2016 IEEE 6th International Conference on Power Systems (ICPS) (pp. 1-5). IEEE Publishing. https://doi.org/10.1109/ICPES.2016.7584000

• Cabrera, N. G., Gutiérrez-Alcaraz, G., & Gil, E. (2013). Load forecasting assessment using SARIMA model and fuzzy inductive reasoning. In 2013 IEEE International Conference on Industrial Engineering and Engineering Management (pp. 561-565). IEEE Publishing. https://doi.org/10.1109/IEEM.2013.6962474

• Chen, Y., Xu, P., Chu, Y., Li, W., Wu, Y., Ni, L., Bao, Y., & Wang, K. (2017). Short-term electrical load forecasting using the support vector regression (SVR) model to calculate the demand response baseline for office buildings. Applied Energy, 195, 659-670. https://doi.org/10.1016/j.apenergy.2017.03.034

• CIGRE Brochure 323. (2007). Ageing of cellulose in mineral-oil insulated transformers. In D1 Materials and Emerging Test Techniques. CIGRE. https://www.e-cigre.org/publications/detail/323-ageing-of-cellulose-in-mineral-oil-insulated-transformers.html,

• CIGRE Brochure 393. (2009). Thermal performance of transformers. In A2 Power Transformers and Reactors. CIGRE. https://www.e-cigre.org/publications/detail/393-thermal-performance-of-transformers.html

• CIGRE Brochure 738. (2018). Ageing of liquid impregnated cellulose for power transformers. In D1 Materials and Emerging Test Techniques. CIGRE. https://www.e-cigre.org/publications/detail/738-ageing-of-liquid-impregnated-cellulose-for-power-transformers.html

• Ese, M. H. G., Liland, K. B., & Lundgaard, L. E. (2010). Oxidation of paper insulation in transformers. IEEE Transactions on Dielectrics and Electrical Insulation, 17(3), 939-946. https://doi.org/10.1109/TDEI.2010.5492270

• Feng, D. (2013). Life Expectancy Investigation of Transmission Power Transformers (Doctoral dissertation). The University of Manchester, England. https://www.escholar.manchester.ac.uk/api/datastream?publicationPid=uk-ac-man-scw:187566&datastreamId=FULL-TEXT.PDF

• Hosseinkhanloo, M., Kalantari, N. T., Behjat, V., & Ravadanegh, S. N. (2022). Optimal exploitation of power transformer fleet considering loss of life and economic evaluation based on failure probability. Electric Power Systems Research, 213, Article 108801. https://doi.org/10.1016/j.epsr.2022.108801

• Hou, J., Wang, Y., Zhou, J., & Tian, Q. (2022). Prediction of hourly air temperature based on CNN-LSTM. Geomatics, Natural Hazards and Risk, 13(1), 1962-1986. https://doi.org/10.1080/19475705.2022.2102942

• Hou, T., Fang, R., Tang, J., Ge, G., Yang, D., Liu, J., & Zhang, W. (2021). A novel short-term residential electric load forecasting method based on adaptive load aggregation and deep learning algorithms. Energies, 14(22), Article 7820. https://doi.org/10.3390/en14227820

• Khalid, R., Javaid, N., Al-zahrani, F. A., Aurangzeb, K., Qazi, E. U. H., & Ashfaq, T. (2020). Electricity load and price forecasting using Jaya-Long Short Term Memory (JLSTM) in smart grids. Entropy, 22, Article 10. https://doi.org/10.3390/e22010010

• Khorsheed, E. (2021). Energy load forecasting: Bayesian and exponential smoothing hybrid methodology. International Journal of Energy Sector Management, 15(2), 294-308. https://doi.org/10.1108/IJESM-06-2019-0005

• Li, J., Zhang, J., Wang, F., Huang, Z., & Zhou, Q. (2018). A novel aging indicator of transformer paper insulation based on dispersion staining colors of cellulose fibers in oil. IEEE Electrical Insulation Magazine, 34(4), 8-16. https://doi.org/10.1109/MEI.2018.8430039

• Liao, R. J., Yang, L. J., Li, J., & Grzybowski, S. (2011). Aging condition assessment of transformer oil-paper insulation model based on partial discharge analysis. IEEE Transactions on Dielectrics and Electrical Insulation, 18(1), 303-311. https://doi.org/10.1109/TDEI.2011.5704522

• Liu, J., Liao, R., Zhang, Y., Gong, C., Wang, C., & Gao, J. (2015). Condition evaluation for aging state of transformer oil-paper insulation based on time-frequency domain dielectric characteristics. Electric Power Components and Systems, 43(7), 759-769. https://doi.org/10.1080/15325008.2014.991462

• Lundgaard, L. E., Hansen, W., & Ingebrigtsen, S. (2008). Ageing of mineral oil impregnated cellulose by acid catalysis. IEEE Transactions on Dielectrics and Electrical Insulation, 15(2), 540-546. https://doi.org/10.1109/TDEI.2008.4483475

• Ma, X., Fang, C., & Ji, J. (2020). Prediction of outdoor air temperature and humidity using Xgboost. IOP Conference Series: Earth and Environmental Science, 427, Article 012013. https://doi.org/10.1088/1755-1315/427/1/012013

• Mohammed, N. A., & Al-Bazi, A. (2022). An adaptive backpropagation algorithm for long-term electricity load forecasting. Neural Computing and Applications, 34, 477-491. https://doi.org/10.1007/s00521-021-06384-x

• Najdenkoski, K., Rafajlovski, G., & Dimcev, V. (2007). Thermal aging of distribution transformers according to IEEE and IEC standards. In 2007 IEEE Power Engineering Society General Meeting (pp. 1-5). IEEE Publishing. https://doi.org/10.1109/PES.2007.385642

• Novkovic, M., Popovic, A., Briosso, E., Iglesias, R. M., & Radakovic, Z. (2022). Dynamic thermal model of liquid-immersed shell-type transformers. International Journal of Electrical Power & Energy Systems, 142, Article 108347. https://doi.org/10.1016/j.ijepes.2022.108347

• Piatniczka, A., Kockott, M., Bistaffa, G., & Paduraru, S. (2022). Transformer loss of life monitoring: A review of in-service highlighting achieved benefits. In 2022 75th Annual Conference for Protective Relay Engineers (CPRE) (pp. 1-7). IEEE Publishing. https://doi.org/10.1109/CPRE55809.2022.9776562

• Radhika, Y., & Shashi, M. (2009). Atmospheric temperature prediction using support vector machines. International Journal of Computer Theory and Engineering, 1(1), 55-58. https://doi.org/10.7763/ijcte.2009.v1.9

• Saleh, N., Azis, N., Jasni, J., Kadir, M. Z. A. A., & Talib, M. A. (2021). Prediction of a transformer’s loading and ambient temperature based on SARIMA approach for hot-spot temperature and loss-of-life analyses. In IEEE International Conference on the Properties and Applications of Dielectric Materials (ICPADM) (pp. 123-126). IEEE Publishing. https://doi.org/10.1109/ICPADM49635.2021.9493865

• Saleh, N., Azis, N., Jasni, J., Kadir, M. Z. A. A., & Talib, M. A. (2022). Paper lifetime mathematical modelling based on multi pre-exponential factors for oil-immersed transformer. Pertanika Journal of Science and Technology, 30(2), 1115-1133. https://doi.org/10.47836/pjst.30.2.15

• Sinha, A., Tayal, R., Vyas, A., Pandey, P., & Vyas, O. P. (2021). Forecasting electricity load with hybrid scalable model based on stacked non linear residual approach. Frontiers in Energy Research, 9, 1-17. https://doi.org/10.3389/fenrg.2021.720406

• Susa, D., Lehtonen, M., & Nordman, H. (2005a). Dynamic thermal modeling of distribution transformers. IEEE Transactions on Power Delivery, 20(3), 1919-1929. https://doi.org/10.1109/TPWRD.2005.848675

• Susa, D., Lehtonen, M., & Nordman, H. (2005b). Dynamic thermal modelling of power transformers. IEEE Transactions on Power Delivery, 20(1), 197-204. https://doi.org/10.1109/TPWRD.2004.835255

• Teymouri, A., & Vahidi, B. (2019). Estimation of power transformer remaining life from activation energy and pre-exponential factor in the Arrhenius equation. Cellulose, 26, 9709-9720. https://doi.org/10.1007/s10570-019-02746-w

• Tripathy, D. S., & Prusty, B. R. (2021). Quantile regression averaging-based probabilistic forecasting of daily ambient temperature. International Journal of Numerical Modelling: Electronic Networks, Devices and Fields, 34(3), 1-19. https://doi.org/10.1002/jnm.2846

• Van den Berg, A. P. B., Bootsma, L. R., Bovenber, T. F. A., Moerbeek, A. R., De Jong, E., Khalil, S., Koch, T., & Dugundji, E. R. (2022). Year-ahead ambient temperature forecasting in pharmaceutical transport lanes thermal conditions. Procedia Computer Science, 201, 255-264. https://doi.org/10.1016/j.procs.2022.03.035

• Zhang, E., Zheng, H., Zhang, C., Wang, J., Shi, K., Guo, J., Schwarz, H., & Zhang, C. (2021). Aging state assessment of transformer cellulosic paper insulation using multivariate chemical indicators. Cellulose, 28. https://doi.org/10.1007/s10570-021-03683-3