Research Article Open Access

Predicting the Remaining Lifetime of Distribution Transformers using Machine Learning

Ntiminity Abontakoyah Enoch1, Puguo Gbene George1 and Justice Aning2
  • 1 Sunyani Technical University, Sunyani, Ghana
  • 2 Sunyani Technical University, Ghana


Distribution Transformer is a crucial element in deciding the power flow in large power systems. Their better performance implies high power system efficiency and enhanced power transfer capability. However, various Distribution Transformer failures in the recent past lead to power supply disturbance and have acquired much attention from the electrical intellectuals. It is of considerable significance to accurately get the running state of distribution transformers and timely detect the existence of potential transformer faults. This project work presents a predictive model to predict the potential of a distribution transformer failing before its expected years in service. Using Random Forest machine learning techniques, we examine transformer data from August 2010 to June 2019. Our experimental results reveal that a total of 90 distribution transformers were damaged within nine years. Thus, average the company losses ten (10) transformer in a year, which amount to the US $92300-95770 per year. Also, most of the places that recorded rate of distribution transformer damage were a location that had mini and major factories around. Thus, the Sunyani Municipality recorded the highest transformer damage (12), representing 13%, followed by Mim (10). Again, lighting strike was the significant causes of transformer damage. Thus twenty-one (21) out of the ninety (90) damage transformers was caused by a lightning strike. The results further show that 33.33% of the damage transformers were with 24.75-36.75% of their life expectancy. As low as 3.33% of the damage transformers have been in service for 73% of the life expectancy. From the study results, it can be concluded that a high percentage (68.9%) of the damage transformers in the Bono, Bono East and Ahafo regions of Ghana have been in service less the half of its expected years of service. Rate-of-faulty-occurrence, Type-of-faults-sustained and Tap-changer-type are the most significant factors that determine the number of years left for a distribution transformer to fail. We observed that the make of a transformer was of less importance in predicting the years left for a transformer to fail. Finally, the RMSE of 0.001639 and MAPE error of 0.001321 achieved by the proposed model shows that the proposed model fits very well to the dataset.

American Journal of Engineering and Applied Sciences
Volume 13 No. 4, 2020, 627-638


Submitted On: 12 August 2020 Published On: 1 January 2021

How to Cite: Enoch, N. A., George, P. G. & Aning, J. (2020). Predicting the Remaining Lifetime of Distribution Transformers using Machine Learning. American Journal of Engineering and Applied Sciences, 13(4), 627-638.

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  • Distribution Transformer
  • Machine Learning
  • Random Forest