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PHMHydro Performance and Health Monitoring for Hydroelectric Powerplants

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Project description

Hydropower plants are the backbone of the Norwegian power system, contributing more than 90% of the total normal annual production. Due to Norway’s unique natural landscape and water resources, these power plants have played a significant role in the country’s industrial development over the past century. As the EU aims for greater reliance on renewable energy—targeting 32% of total energy consumption from renewable sources by 2030—Norway has the capacity to play a larger role in the EU grid as "Europe’s green battery."

The power grid of the future requires stable and reliable energy sources that can quickly meet demand when, for example, wind power production is low, and store excess energy when wind power production is high. Norway is rich in hydropower and has the capacity to meet these requirements, as power production can be ramped up quickly, and energy can be stored, for instance, by pumping water back into reservoirs. However, to achieve this, hydropower plants must maintain high reliability and efficiency to ensure a stable supply—i.e., a stable power grid. Continuous monitoring of the condition of critical equipment such as turbines and generators, as well as the efficiency of the equipment, ensures that hydropower plants operate profitably and without unwanted shutdowns.

Participants

Publications

  • Shanbhag, Vignesh Vishnudas; Kandukuri, Surya Teja; Olimstad, Grunde & Schlanbusch, Rune (2025). Predictive maintenance of critical components in hydroelectric turbines: A review. IEEE Sensors Journal. ISSN 1530-437X. 25(17), p. 31959–31979. doi: 10.1109/JSEN.2025.3577692. Full text in Research Archive
  • Kandukuri, Surya Teja; Tangrand, Kristoffer Meyer; Vognild, Lars Kristian & Olimstad, Grunde (2024). Automated Knowledge Graph Generation for Hydropower Plants using ISO 81346 and RAG-enhanced Large Language Models. In Gragger, Johannes; Colak, Ilhami & Kurokaw, Fujio (Ed.), ICRERA 2025. IEEE. ISSN 9798331599898. doi: 10.1109/icrera66237.2025.11284013. Full text in Research Archive

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  • Nguyen, Thien Phuoc; Mahmoud, Mahmoud S.; Huynh, Van Khang; Tran, Thi-Minh-Dung & Robbersmyr, Kjell G. (2025). Analytical Model-Augmented Supervised Machine Learning for Fault Diagnosis of WFSG with Limited Fault Samples. doi: 10.23919/icems66262.2025.11316950. Full text in Research Archive
  • Nguyen, Phuoc; Islam, Anfaj; Han, Zehan; Huynh, Van Khang; Jha, Ajit & Robbersmyr, Kjell G. (2025). Multiple-winding Fault Classification in Wound Field Synchronous Generators Using Vibration Signals. doi: 10.23919/icems66262.2025.11317164. Full text in Research Archive
  • Kandukuri, Surya Teja; Vognild, Lars Kristian; Schlanbusch, Rune & Olimstad, Grunde (2024). An investigation into the use of RDS-PS (ISO81346-10) for hydropower condition monitoring and performance analytics. Full text in Research Archive

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Funding

Funded by The Research Council of Norway

Project number: 333871

Published Feb. 27, 2025 1:26 PM - Last modified Aug. 13, 2025 12:10 PM