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

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Marulanda Guerra, Agustin Rafael
Benavides Navarro, Luis Daniel
Duran Tovar, Ivan Camilo
Abstract (English)
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.
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6 p.
URI: https://repositorio.escuelaing.edu.co/handle/001/3509
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AE - Modelación Estratégica en Energía y Potencia – MEEP
References

IRENA, "Renewable energy statistics 2022," International Renewable Energy Agency, Abu Dhabi, 2022.

T. De Silva, J. Jorgenson, J. Macknick, N. Keohan, A. Miara, H. Jager y B. Pracheil, «Hydropower operation in future power grid with various renewable power integration,» Renewable Energy Focus, vol. 43, pp. 329- 339, 2023.

J. Zhang, C. Cheng, S. Yu, J. Shen, . X. Wu y H. Su, «Preliminary feasibility analysis for remaking the function of cascade hydropower stations to enhance hydropower flexibility: A case study in China,» Energy, vol. 260, p. 125163, 2022

J. Ren, L. Zhang, L. Jin, J. He y Y. Gao, «Digital Transformation of Hydropower Stations: Technical Route, Maturity Evaluation and Content Planning,» de 2022 IEEE 5th International Electrical and Energy Conference (CIEEC), 2022.

V. Gupta , R. Mitra, F. Koenig, M. Kumar y . M. Kumar Tiwari, «Predictive maintenance of baggage handling conveyors using IoT,» Computers & Industrial Engineering, vol. 177, p. 109033, 2023

K. Kumar y . R. P. Saini, «A review on operation and maintenance of hydropower plants,» Sustainable Energy Technologies and Assessments, vol. 49, p. 101704, 2022.

A. Yasir Saleem , A. Kashif y H. Laiq, «Artificial intelligence based prognostic maintenance of renewable energy systems: A review of techniques, challenges, and future research directions,» International Journal of Energy Research, vol. 46, nº 15, pp. 21619-21642, 2022.

G.-P. Liao, W. Gao, G.-J. Yang y M.-F. Mou-Fa, «Hydroelectric Generating Unit Fault Diagnosis Using 1-D Convolutional Neural Network and Gated Recurrent Unit in Small Hydro,» IEEE Sensors Journal, vol. 19, nº 20, pp. 9352-9363, 2019.

IEA, «Colombia 2023. Energy Policy Review,» International Energy Agency, 2023.

X. Zhu, T. Bao, J. K. W. Yeoh, N. Jia y H. Li, «Enhancing dam safety evaluation using dam digital twin,» Structure and Infrastructure Engineering, vol. 19, nº 7, pp. 904-920, 2023.

P. Xu, Z. Wang y V. Li, «Prognostics and Health Management (PHM) System requirements and validation,» 2010 Prognostics and System Health Management Conference, pp. 1-4, 2010.

A. Muller, A. Crespo Marquez y B. Iung, «On the concept of e-maintenance. Information and Communication technologies applied to maintenance: Review and current research,» Reliability Engineering & System Safety, vol. 93, nº 8, pp. 1165-1187, 2008

E. Jantunen, C. Emmanouilidis, A. Arnaiz y E. Gilabert, «eMaintenance: trends, challenges and opportunities for modern industry,» IFAC Proceedings Volumes (2011), vol. 44, pp. 453-458, 2011

M. A. Hariri-Ardebili, G. Mahdavi, L. Nuss y U. Lall, «The role of artificial intelligence and digital technologies in dam engineering: Narrative review and outlook,» Engineering Applications of Artificial Intelligence, vol. 126, 2023.

A. Betti, E. Crisostomi, G. Paolinelli, A. Piazzi, F. Ruffini y M. Tucci, «Condition monitoring and predictive maintenance methodologies for hydropower plants equipment,» Renewable Energy, vol. 171, pp. 246-253, 2021.

Y. Afridi, K. Ahmad y L. Hassan, «Artificial intelligence based prognostic maintenance of renewable energy systems: A review of techniques, challenges, and future research directions,» International Journal of Energy Research, vol. 46, nº 15, pp. 21619-21642, 2021.

M. Fahim, V. Sharma, T.-V. Cao, B. Canberk y T. Q. Duong, «Machine Learning-Based Digital Twin for Predictive Modeling in Wind Turbines,» IEEE Access, vol. 10, pp. 14184-14194, 2022.

H. Zhang, B. Liu y H. Wu, «Smart Grid Cyber-Physical Attack and Defense: A Review,» IEEE Access, vol. 9, pp. 29641-29659, 2021.