Assets Performance Management systems for hydroelectric power plants—A survey

Abstract

This paper presents a survey about several works in the field of predictive maintenance for hydroelectric power plants related to Assets Performance Management systems. It discusses about how to make a system study to better understand the structure of the Hydropower Plants and its components and how to define the most significant items to the maintenance. Also, it presents predictive maintenance techniques in terms of failure detection, diagnosis and prognostics. The research shows that data-driven methods are increasingly used by Hydropower Plants. With the recent development of artificial intelligence methods, it is possible to discover unknown patterns in sensor data and learn existing relationships in the data that may not be easy for a human to discover. In this way, some hybrid methods, which mix concepts such as the transformation of time series data into the frequency domain, statistical modeling and machine learning, are being developed to perform the tasks of APM systems. The work also shows the possibility of developing methods focusing on reinforcement learning or explainable artificial intelligence for the industrial sector. Such advances can count as a contribution to the innovative nature of the electricity sector, which tends to become increasingly sustainable in order to mitigate the environmental impacts caused by production.