Predicting hydro turbine failures through digital twin simulations of rare real-world data

Abstract

This paper aims to establish a predictive model for hydro turbine failures by simulating rare real-world data using a digital twin system. Hydro turbines play a critical role in the renewable energy sector, but their unpredictability in terms of failures results in significant maintenance and operational costs. The traditional fault prediction method based on historical data is difficult to achieve more accurate and generalized modeling, because the data in the real world cannot meet the requirements of the machine learning theory for the same distribution of data and data balance, especially some rare events are difficult to collect in reality. Therefore, in this paper, it is proposed to enhance the robustness of hydro turbine failure prediction by simulating data from some rare situations through a digital twin system. By collecting and simulating rare data from actual hydroelectric turbines, we gain a better understanding of their operational mechanisms and fault patterns. We propose a digital twin system capable of replicating real-world operating conditions in a virtual environment, which serves as the foundation for data-driven fault prediction models. Through deep learning analysis of the simulated data, we can predict the likelihood of hydro turbine failures, thus improving maintenance strategies, reducing costs, and enhancing turbine reliability. Our research offers a promising approach to addressing rare data challenges using digital twin systems and holds broad application potential within the hydropower industry.