Machine learning for predictive maintenance scheduling of distribution transformers

Abstract

PurposeThe purpose of this paper is to describe a methodology that has been set up to schedule predictive maintenance of distribution transformers at Cauca Department (Colombia) using machine learning.Design/methodology/approachThe proposed methodology relies on classification predictive model that finds the minimal number of distribution transformers prone to failure. To verify this, the model was implemented and tested with real data in Cauca Department Colombia.FindingsThe implementation of the methodology allows a saving of 13% in corrective maintenance expenses for the year 2020.Originality/valueThe proposed model is an effective decision-making tool that provides an ideal solution for preventive maintenance scheduling problems for distribution transformers.

Predictive Maintenance for Distribution System Operators in Increasing Transformers’ Reliability

“…We can infer from the foregoing that the reported terms under consideration should not have a significant impact on the transformers' technical problems. Additionally, the learning accuracy is higher than this presented in [33], in which it was 95.43% for 2019 and 90.62% for 2020.…”

“…As can be seen in Figure 8 and Table 3, the proposed methodology is compared to the methodology proposed in [33]. The methodology proposed in the current work predicts in general in every transformer's power, rating a smaller number of transformers that may present problems in comparison to [33]. The total predicted burned transformers for the methodology presented in [33] is 910, while for the AI methodology presented in this work, the number is 852.…”

“…As can be seen in Figure 8 and Table 3, the proposed methodology is compared to the methodology proposed in [33]. The methodology proposed in the current work predicts in general in every transformer's power, rating a smaller number of transformers that may present problems in comparison to [33].…”

“…According to data from Compania Energetica de Occidente, 1297 transformers burned in 2016; replacement required substantial expenses and power supply disruptions [32,33]. There have been several recorded causes of burning, including atmospheric discharges, third-party manipulation, overload, and lack of secondary distribution line trimming.…”

“…Major types of faults [18,20] include high-and low-energy arcing, partial discharge, and hot patches with a range of temperatures, but if negligent implementation is used, there is no region for a normal aging condition that might lead to a diagnostic of any of the faults. To avoid these issues, researchers collected different types of data than the insulating oil parameters for the power distribution transformers from the Compania Energetica de Occidente [32,33]. The variables were binary, continuous and categorical, such as the keraunic-level criticality or the earth discharge It is feasible to suggest cost reduction, machine failure and repair reduction, and inventory reduction methods through the treatment and analysis of these data [24].…”