Evaluating Deep Learning Networks Versus Hybrid Network for Smart Monitoring of Hydropower Plants

Fatemeh Hajimohammadali, Emanuele Crisostomi, Mauro Tucci and Nunzia Fontana ()
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Fatemeh Hajimohammadali: Department of Energy, Systems, Territory and Constructions Engineering, University of Pisa, 56122 Pisa, Italy
Emanuele Crisostomi: Department of Energy, Systems, Territory and Constructions Engineering, University of Pisa, 56122 Pisa, Italy
Mauro Tucci: Department of Energy, Systems, Territory and Constructions Engineering, University of Pisa, 56122 Pisa, Italy
Nunzia Fontana: Department of Energy, Systems, Territory and Constructions Engineering, University of Pisa, 56122 Pisa, Italy

Energies, 2024, vol. 17, issue 22, 1-16

Abstract: One of the main goals of the International Energy Agency (IEA) is to manage and utilize clean energy to achieve net zero emissions by 2050. Hydropower plants can significantly contribute to this goal as they are vital components of the global energy infrastructure, providing a clean, safe, and sustainable power source. Accordingly, there is great interest in developing methods to prevent errors and anomalies and ensure full operational availability. With modern hydropower plants equipped with sensors that capture extensive data, machine learning algorithms utilizing these data to detect and predict anomalies have gained research attention. This paper demonstrates that deep learning algorithms are particularly powerful in predicting time series. Three well-known deep learning networks are examined and compared to previous approaches, followed by the introduction of a new, innovative hybrid network. Using real-world data from two hydropower plants, the hybrid model outperforms individual deep learning models by achieving more accurate fault detection, reducing false positives, offering early fault prediction, and identifying faults several weeks before occurrence. These results showcase the hybrid network’s potential to enhance maintenance planning, reduce downtime, and improve operational efficiency in energy systems.

Keywords: hydropower plants; process control; predictive maintenance; fault detection; hybrid deep learning models; time series forecasting (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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