A REVIEW OF EFFICIENCY IMPROVEMENT OF HYDRO-TURBINE GENERATOR

Authors

  • Muhammad Irfan Syahir Zarawi Faculty of Electrical Engineering, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia
  • Muhammad Afnan Afian Faculty of Electrical Engineering, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia
  • Muhammad Ammar Hazim Muhammad Rezal Faculty of Electrical Engineering, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia
  • Muhammad Aiman Muhammad Raie Faculty of Electrical Engineering, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia
  • Ng Zhi Jun Faculty of Electrical Engineering, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia
  • Joshua Niiroshan Andrew Ramu Faculty of Electrical Engineering, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia
  • Nur Ayeesha Qisteena UTM
  • Norazliani binti Md Sapari

DOI:

https://doi.org/10.11113/jest.v8.235

Keywords:

hydroelectric power, hydro turbine efficiency, hydropower optimization, renewable energy

Abstract

Endowed with favorable topography and tremendous amount of rainfall annually, allowed Malaysia to boost its hydroelectric power plant with Bakun Hydroelectric Dam in Sarawak as the largest supplier of hydroelectric energy. Hydroelectric not only acts as flood mitigation but also an alternative of electricity that slowly replaces conventional electric sources like natural gases and coal. However, efficiency of hydro-turbine should be taken into consideration to minimize energy loss while simultaneously optimizing total output power.  The review also highlights major challenges, such as material limitations, infrastructure constraints, and the complexities of integrating artificial intelligence into control and optimization processes. Overall, the findings show that improving system efficiency requires simultaneous advancements in material selection, monitoring technologies, control strategies, and predictive tools to create more reliable and higher‑performing hydropower systems.

References

Moran, M. J., Shapiro, H. N., Boettner, D. D., & Bailey, M. B. (2014). Fundamentals of engineering thermodynamics (8th ed.). Wiley.

Kaunda, C. S. (2012). Energy management and efficiency in hydropower systems. Renewable and Sustainable Energy Reviews, 16(7), 4982–4990.

Gundabattini, E., Kuppan, R., Solomon, D. G., Kalam, A., Kothari, D., & Bakar, R. A. (2021). A review on methods of finding losses and cooling methods to increase efficiency of electric machines. Ain Shams Engineering Journal, 12(1), 497–505.

Mörée, G., & Leijon, M. (2024). Iron loss models: A review of simplified models of magnetization losses in electrical machines. Journal of Magnetism and Magnetic Materials, 609, 172163.

Kühn, P., Yang, Y., Chen, G., Foster, S. N., Egger, H., & Xu, B. (2025). Multiphysics simulations of microstructure influence on hysteresis and eddy current losses of electrical steel. arXiv (Cornell University).

Çengel, Y. A., & Cimbala, J. M. (2014). Fluid mechanics: Fundamentals and applications (3rd ed.). McGraw-Hill.

Vasudevan, K. R., Ramachandaramurthy, V., Venugopal, G., Ekanayake, J., & Tiong, S. K. (2021). Variable speed pumped hydro storage: A review of converters, controls and energy management strategies. Renewable and Sustainable Energy Reviews, 135, 110156.

K. E. Okedu, M. Al Tobi, and S. Al Araimi, “Comparative study of the effects of machine parameters on DFIG and PMSG variable speed wind turbines during grid fault,” Front. Energy Res., vol. 9, Art. no. 681443, May 2021, doi: 10.3389/fenrg.2021.681443.

V. Mohale and T. R. Chelliah, “Impact of fixed/variable speed hydro, wind, and photovoltaic on sub-synchronous torsional oscillation—A review,” Sustainability, vol. 15, no. 1, p. 113, Dec. 2022, doi: 10.3390/su15010113.

Aubert, S. H., Ladreiter-Knauss, C., Polster, S., & Steinmann, P. (2025). Converting the Malta Oberstufe pumped storage plant to variable speed with full converter during an overhaul. Factor This.

Cebeci, C., Parker, M., Recalde-Camacho, L., Campos-Gaona, D., & Anaya-Lara, O. (2025). Variable-speed hydropower control and ancillary services: A remedy for enhancing grid stability and flexibility. Energies, 18(3), 642.

Mobley, S. (2002). An introduction to predictive maintenance. Elsevier.

Heng, A., Zhang, S., Tan, A., & Mathew, J. (2009). Rotating machinery prognostics: State of the art, challenges and opportunities. Mechanical Systems and Signal Processing, 23(3), 724–739.

Lei, Y., Li, N., Guo, L., Yan, N., & Lin, J. (2018). Machinery health prognostics using deep learning: A review. Mechanical Systems and Signal Processing, 104, 469–498.

ABB Group. (2021). ABB Ability™ condition monitoring for generators (Technical report).

Qasim, M. A., & Hanfesh, A. (2017). The effect of speed smart control system (SSCS) on the performance of hydropower. Information Technology Journal, 35.

Janevska, G., & Panovski, S. (2019). Aspects on modeling of hydro-turbine speed governor. International Journal of Scientific & Engineering Research, 10(6), 34.

Wang, C., Wang, D.-K., & Zhang, J.-M. (2021). Experimental study on the optimal strategy for power regulation of governing system of hydropower station. Water, 13(4), 421.

García Márquez, F. P., et al. (2026). A comprehensive review of condition monitoring systems for hydropower stations: Technologies, applications, and future trends. Electric Power Systems Research, 251, 112339.

Mohanta, R. K., et al. (2017). Sources of vibration and their treatment in hydro power stations: A review. Engineering Science and Technology, an International Journal, 20(2), 637–648.

Betti, A., et al. (2021). Condition monitoring and predictive maintenance methodologies for hydropower plants equipment. Renewable Energy, 171, 246–253.

Syafiudin, M., Arifuddin, R., & Kartika Sari, R. D. J. (2025). Analysis of penstock design and losses in micro-hydro power plant at Wisata Telaga River, Malang. Journal of Renewable Energy and Mechanics.

Pepe, C., & Zanoli, S. M. (2024). Digitalization, Industry 4.0, data, KPIs, modelization and forecast for energy production in hydroelectric power plants: A review. Energies, 17(4), 941.

Hanoon, M. S., Ahmed, A. N., Razzaq, A., Oudah, A. Y., Alkhayyat, A., Huang, Y. F., & El-Shafie, A. (2023). Prediction of hydropower generation via machine learning algorithms at Three Gorges Dam, China. Ain Shams Engineering Journal, 14(4), 101919.

NTNU. (2025). Predictive maintenance and analytics in hydroelectric power (Open thesis/technical report). NTNU Open.

Bernardes, J., Jr., Santos, M., Abreu, T., Prado, L., Jr., Miranda, D., Julio, R., Viana, P., Fonseca, M., Bortoni, E., & Bastos, G. S. (2022). Hydropower operation optimization using machine learning: A systematic review. Energies, 15(12), 4567.

A. Christe, A. Faulstich, M. Vasiladiotis, and P. Steinmann, “World’s first fully rated direct ac/ac MMC for variable-speed pumped-storage hydropower plants,” IEEE Trans. Ind. Electron., vol. 70, no. 7, pp. 6898–6907, Jul. 2023, doi: 10.1109/TIE.2022.3204858.

S. Aubert, C. Häderli, C. Ladreiter-Knauss, S. Polster, and P. Steinmann, “Malta Oberstufe overhaul project – Variable speed operation with MMC full converter,” in Proc. Vienna Hydro 2022, Vienna, Austria, Nov. 2022.

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Published

2025-12-23

How to Cite

Syahir Zarawi, M. I., Afian, M. A., Muhammad Rezal, M. A. H., Muhammad Raie, M. A., Jun, N. Z., Andrew Ramu, J. N., … Md Sapari, N. binti. (2025). A REVIEW OF EFFICIENCY IMPROVEMENT OF HYDRO-TURBINE GENERATOR. Journal of Energy and Safety Technology (JEST), 8(2), 113–120. https://doi.org/10.11113/jest.v8.235