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Article

Hydropower Station Status Prediction Using RNN and LSTM Algorithms for Fault Detection

by
Omar Farhan Al-Hardanee
1,2,3,* and
Hüseyin Demirel
1,4
1
Department of Electrical and Electronics Engineering, Karabük University, Karabük 78050, Türkiye
2
Upper Euphrates Basin Developing Center, University of Anbar, Ramadi 31001, Iraq
3
College of Engineering, University of Anbar, Ramadi 31001, Iraq
4
Department of Management Information Systems, Business School, Ankara Yıldırım Beyazıt University, Ankara 06760, Türkiye
*
Author to whom correspondence should be addressed.
Energies 2024, 17(22), 5599; https://doi.org/10.3390/en17225599
Submission received: 30 June 2024 / Revised: 26 July 2024 / Accepted: 26 July 2024 / Published: 9 November 2024
(This article belongs to the Section F: Electrical Engineering)

Abstract

In 2019, more than 16% of the globe’s total production of electricity was provided by hydroelectric power plants. The core of a typical hydroelectric power plant is the turbine. Turbines are subjected to high levels of pressure, vibration, high temperatures, and air gaps as water passes through them. Turbine blades weighing several tons break due to this surge, a tragic accident because of the massive damage they cause. This research aims to develop predictive models to accurately predict the status of hydroelectric power plants based on real stored data for all factors affecting the status of these plants. The importance of having a typical predictive model for the future status of these plants lies in avoiding turbine blade breakage and catastrophic accidents in power plants and the resulting damages, increasing the life of these plants, avoiding sudden shutdowns, and ensuring stability in the generation of electrical energy. In this study, artificial neural network algorithms (RNN and LSTM) are used to predict the condition of the hydropower station, identify the fault before it occurs, and avoid it. After testing, the LSTM algorithm achieved the greatest results with regard to the highest accuracy and least error. According to the findings, the LSTM model attained an accuracy of 99.55%, a mean square error (MSE) of 0.0072, and a mean absolute error (MAE) of 0.0053.
Keywords: RNN; LSTM; forecasting; vibration; temperature; pressure; turbine; dam RNN; LSTM; forecasting; vibration; temperature; pressure; turbine; dam

Share and Cite

MDPI and ACS Style

Al-Hardanee, O.F.; Demirel, H. Hydropower Station Status Prediction Using RNN and LSTM Algorithms for Fault Detection. Energies 2024, 17, 5599. https://doi.org/10.3390/en17225599

AMA Style

Al-Hardanee OF, Demirel H. Hydropower Station Status Prediction Using RNN and LSTM Algorithms for Fault Detection. Energies. 2024; 17(22):5599. https://doi.org/10.3390/en17225599

Chicago/Turabian Style

Al-Hardanee, Omar Farhan, and Hüseyin Demirel. 2024. "Hydropower Station Status Prediction Using RNN and LSTM Algorithms for Fault Detection" Energies 17, no. 22: 5599. https://doi.org/10.3390/en17225599

APA Style

Al-Hardanee, O. F., & Demirel, H. (2024). Hydropower Station Status Prediction Using RNN and LSTM Algorithms for Fault Detection. Energies, 17(22), 5599. https://doi.org/10.3390/en17225599

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