E-maintenance in hydropower energy generation: A case study of Enel Colombia

Abstract

Traditionally, maintenance in the hydropower industry has been a labour-intensive and time-consuming process. It often relies on scheduled inspections and manual intervention. E-maintenance in hydropower plants can help to address this challenge by allowing remote monitoring and control of plant equipment, enabling timely detection and diagnosis of potential problems. This paper presents a case study of the implementation of an e-maintenance strategy for hydropower infrastructure at one of the largest generation companies in the Colombian electricity market.  A machine learning model, implemented by Enel Colombia, is fed with recorded data on turbine bearing temperature and active power generation to predict problems in hydropower generators. The results show how e-maintenance can reduce operating costs and avoid breakdowns in hydro generation.