Predictive Maintenance for Distribution System Operators in Increasing Transformers’ Reliability

Power transformers’ reliability is of the highest importance for distribution networks. A possible failure them can interrupt supply to consumers, which will cause inconvenience and loss revenue electricity companies. Additionally, depending on type damage, recovery time vary intensify problems consumers. This paper estimates maintenance required transformers using Artificial Intelligence (AI). way condition equipment that currently in use evaluated should be performed known. Because actions are only carried out when necessary, this strategy promises cost reductions over routine or time-based preventative maintenance. The suggested methodology uses a classification predictive model identify with high accuracy number vulnerable failure. was confirmed by training, testing, validating it actual data Colombia’s Cauca Department. It clear from experimental method Machine Learning (ML) methods early detection technical issues help system operators increase selected these also beneficial customers’ satisfaction performance transformers, would enhance highly reliable such transformers. According prediction 2021, 852 malfunction, 820 rural Cauca, consistent previous statistics. 10 kVA most vulnerable, followed 5 15