Dataset of distribution transformers for predictive maintenance

• Published in: Data in brief Vol. 38; p. 107454
• Main Authors: Bravo M, Diego-A, Alvarez Q, Laura-I, Lozano M, Carlos-A
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.10.2021; Elsevier
• Subjects: Data; Distribution transformers; Machine learning; Predictive maintenance; Distribution transformers; Machine learning; Predictive maintenance
• ISSN: 2352-3409; 2352-3409
• DOI: 10.1016/j.dib.2021.107454

In electricity sector is possible to collect large quantities of data that contain information on relevant processes and events that occur in a given period. It gives a knowledge of the different operation conditions of the electrical network and its components. Through the treatment and analysis of these data is possible to propose market, cost reduction, reduction of failures and repairs in machines and inventory decrease strategies. Grid operator can implement strategies to improve indicators of reliability and quality of service. From a maintenance point of view, the equipment operating time is a relevant aspect to identify and solve failures without service suspensions. This paper aims to show distribution transformers failures characteristics data using historical data collected by the grid operator (Compañia Energética de Occidente) at Cauca Department (Colombia), under the cooperation of the Universidad del Cauca and Universidad del Valle. The dataset could be helpful to researchers and data scientists who use machine learning to develop applications that help engineers in predictive maintenance.