Open Access
Issue |
E3S Web Conf.
Volume 573, 2024
2024 International Conference on Sustainable Development and Energy Resources (SDER 2024)
|
|
---|---|---|
Article Number | 03009 | |
Number of page(s) | 5 | |
Section | Sustainable Development and Electricity Market Research | |
DOI | https://doi.org/10.1051/e3sconf/202457303009 | |
Published online | 30 September 2024 |
- Pérez-Díaz, J.I., Chazarra, M., García-González, J., Cavazzini, G., & Stoppato, A. (2015). Trends and challenges in the operation of pumped-storage hydropower plants. Renewable and Sustainable Energy Reviews, 44, 767-784. [CrossRef] [Google Scholar]
- Zhao, J.F., Oh, U.J., Park, J.C., Park, E.S., Im, H.B., Lee, K.Y., & Choi, J.S. (2022). A review of world-wide advanced pumped storage hydropower technologies. IFAC-PapersOnLine, 55(9), 170-174. [CrossRef] [Google Scholar]
- Qin, F., Xia, Y.Q., Hu, B.W. (2023). Analysis and Discussion on the Current Status of Online Monitoring Data Mining Applications for Floodgates. Mechanical & Electrical Technique of Hydropower Station, 46(2), 98-100. [Google Scholar]
- Kong, Y., Kong, Z., Liu, Z., Wei, C., Zhang, J., & An, G. (2017). Pumped storage power stations in China: The past, the present, and the future. Renewable and Sustainable Energy Reviews, 71, 720-731. [CrossRef] [Google Scholar]
- Yuniarti, N., Hariyanto, D., Yatmono, S., & Abdillah, M. (2021). Design and Development of IoT Based Water Flow Monitoring for Pico Hydro Power Plant. Int. J. Interact. Mob. Technol., 15(7), 69-80. [CrossRef] [Google Scholar]
- Betti, A., Crisostomi, E., Paolinelli, G., Piazzi, A., Ruffini, F., & Tucci, M. (2021). Condition monitoring and predictive maintenance methodologies for hydropower plants equipment. Renewable Energy, 171, 246-253. [CrossRef] [Google Scholar]
- ÖzcAn, E., DAnışAn, T., YumuşAk, R., & EREn, T. (2020). An artificial neural network model supported with multi criteria decision making approaches for maintenance planning in hydroelectric power plants. Eksploatacja i Niezawodność, 22(3). [Google Scholar]
- Yang, G.M., & Jia, W.B. (2011). Research to health diagnose model of gate and hoist machinery based on AHP. Advanced Materials Research, 287, 3036-3042. [CrossRef] [Google Scholar]
- Chen, F. (2021). Safety evaluation method of hoisting machinery based on neural network. Neural Computing and Applications, 33(2), 565-576. [CrossRef] [Google Scholar]
- Chen, F. (2021). Safety evaluation method of hoisting machinery based on neural network. Neural Computing and Applications, 33(2), 565-576. [CrossRef] [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.