Predicting the Remaining Lifetime of Distribution Transformers using Machine Learning

Abstract

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-offaulty-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.

Analysis of estimated age of distribution transformer against loading at ULP Panakkukang with the montsinger method

“…The Montsinger method is a statistical approach used to predict the lifetime of power transformers in distribution systems [11]. It is based on historical lifetime information about the transformer population or fleet [12]. The method can account for left-truncated and right-censored lifetime data by analyzing transformers' installation and failure dates, where information about units installed and failed before a specific date is unavailable [11][12][13].…”

“…It is based on historical lifetime information about the transformer population or fleet [12]. The method can account for left-truncated and right-censored lifetime data by analyzing transformers' installation and failure dates, where information about units installed and failed before a specific date is unavailable [11][12][13]. This statistical approach allows for more accurate predictions of remaining life based on the available data.…”

“…The prediction of remaining life for individual transformers is achieved using a parametric lifetime model that describes the lifetime distribution of the transformers [11]. The Montsinger method utilizes age-adjusted life distributions to compute prediction intervals for the remaining life of individual transformers in service [11][12][13][14]. By calibrating the prediction interval of remaining life for an individual transformer with a given age, the Montsinger method provides a statistical estimate of the remaining lifespan [11][12][13][14][15].…”