Deep Learning Approach for Predicting Thermal Behavior of Hydropower Generator-Stator: A Case Study of a Hydropower Power Plant in Thailand
DOI:
https://doi.org/10.59796/jcst.V15N4.2025.145Keywords:
deep learning, thermal behavior prediction, hydropower generator-statorAbstract
Hydropower generation is a cost-effective and environmentally friendly energy source that converts the kinetic energy of flowing water into electricity. However, temperature control in power generators, particularly in the conductor windings in the stator, remains a significant challenge for maintaining power generation performance. Several factors influence temperature, and their relationships are quite complex, making it difficult to solve the problem using standard theoretical approaches. This research developed a deep learning model to monitor temperature trends in the conductor windings of a 125 MW hydropower plant in Thailand. Data collected between 2018 and 2021 on electricity generation, reservoir water levels, water and air flow rates, inlet temperatures at the heat exchanger, and conductor winding temperatures were used to train and validate the models. The study implemented three neural network models: a Feedforward Neural Network (FNN), a Multilayer Feedforward Neural Network (MFNN), and a Long Short-Term Memory (LSTM) network. The results showed that the LSTM model provided the most accurate predictions, with a Mean Squared Error (MSE) of 0.00373. Shapley Additive exPlanations (SHAP) values were used to interpret the model predictions, identifying key variables such as electricity generation, water temperature, and water flow rate as the most influential factors affecting system behavior. The findings suggest that deep learning models can effectively predict temperature variations, enabling proactive maintenance and improving operational efficiency in hydropower plants.
References
Aarons, K., Vine, D., & Center for Climate and Energy Solutions. (2015). Canadian hydropower and the Clean Power Plan. Retrieved from https://www.c2es.org/document/canadian-hydropower-and-the-clean-power-plan/
Al-Selwi, S. M., Hassan, M. F., Abdulkadir, S. J., Muneer, A., Sumiea, E. H., Alqushaibi, A., & Ragab, M. G. (2024). RNN-LSTM: From applications to modeling techniques and beyond Systematic review. Journal of King Saud University-Computer and Information Sciences, 36(5), Article 102068. https://doi.org/10.1016/j.jksuci.2024.102068
Almqvist, A. (2021). Fundamentals of physics-informed neural networks applied to solve the Reynolds boundary value problem. Lubricants, 9(8), Article 82. https://doi.org/10.3390/lubricants9080082
Asvapoositkul, W., & Kuansathan, M. (2016). The effectiveness-NTU approach for evaluation of an air-cooled heat exchanger. Heat Transfer Engineering, 37(2), 140–149. https://doi.org/10.1080/01457632.2015.1044393
Bhattacharyya, S., Sarkar, D., Roy, R., Chakraborty, S., Goel, V., & Almatrafi, E. (2021). Application of new artificial neural network to predict heat transfer and thermal performance of a solar air-heater tube. Sustainability (Switzerland), 13(13), Article 7477. https://doi.org/10.3390/su13137477
Cai, S., Wang, Z., Wang, S., Perdikaris, P., & Karniadakis, G. E. (2021). Physics-informed neural networks for heat transfer problems. Journal of Heat Transfer, 143(6), Article 060801. https://doi.org/10.1115/1.4050542
Chen, H., Lundberg, S. M., & Lee, S. I. (2022). Explaining a series of models by propagating Shapley values. Nature Communications, 13(1), Article 31384. https://doi.org/10.1038/s41467-022-31384-3
Chen, H., Peng, W., Nie, C., Xu, G., & Lei, J. (2021). Performance prediction and optimization of the air-cooled condenser in a large-scale power plant using machine learning. Energy Technology, 9(11), Article 2100045. https://doi.org/10.1002/ente.202100045
Cui, Z., Li, N., & Wu, L. (2021). Multi-objective optimization of the generator air cooler based on genetic algorithm. Journal of Physics: Conference Series, 2030, Article 012079. https://doi.org/10.1088/1742-6596/2030/1/012079
Desingu, K., Chelliah, T. R., & Khare, D. (2018). Investigation on the performance of a 277.8 MVA synchronous air-cooled hydrogenerator through loss models [Conference presentation]. 2018 International Conference on Power, Instrumentation, Control and Computing (PICC), Thrissur, India. https://doi.org/10.1109/PICC.2018.8384790
Ding, B., Qian, H., & Zhou, J. (2018). Activation functions and their characteristics in deep neural networks [Conference presentation]. Proceedings of the 30th Chinese Control and Decision Conference, CCDC 2018, Shenyang, China. https://doi.org/10.1109/CCDC.2018.8407425
Doost, A. K., & Majlessi, R. (2015). Heat transfer analysis in cooling system of hydropower’s generator. Open Journal of Applied Sciences, 5(3), 98–107. https://doi.org/10.4236/OJAPPS.2015.53010
Electricity Generating Authority of Thailand. (n.d.). Renewable energy. Retrieved from https://www.egat.co.th/home/renewables/
Ghettini, S., Sorce, A., & Sacile, R. (2020). Data-driven air-cooled condenser performance assessment: Model and input variable selection comparison. E3S Web of Conferences, 197, Article 10003. https://doi.org/10.1051/e3sconf/202019710003
Goharoodi, S. K., Nguyen Phuc, P., Dupre, L., & Crevecoeur, G. (2019). Data-driven discovery of the heat equation in an induction machine via sparse regression [Conference presentation]. Proceedings of the IEEE International Conference on Industrial Technology, Melbourne, VIC, Australia. https://doi.org/10.1109/ICIT.2019.8754983
He, Z., Ni, F., Wang, W., & Zhang, J. (2021). A physics-informed deep learning method for solving direct and inverse heat conduction problems of materials. Materials Today Communications, 28, Article 102719. https://doi.org/10.1016/j.mtcomm.2021.102719
Jadhav, V., Deodhar, A., Gupta, A., & Runkana, V. (2022). Physics-informed neural network for health monitoring of an air preheater. PHM Society European Conference, 7(1), 219–230. https://doi.org/10.36001/phme.2022.v7i1.3343
Kamble, L. V., Pangavhane, D. R., & Singh, T. P. (2014). Heat transfer studies using artificial neural network – A review. International Energy Journal, 14(1), 25-42. http://www.rericjournal.ait.ac.th/index.php/reric/article/view/1066
Khun, N. S., Deethayat, T., Kiatsiriroat, T., & Asanakham, A. (2025). Strategy for energy savings in a commercial building air-conditioning system with chilled water storage: A case study in a retail mall in Thailand. Journal of Current Science and Technology, 15(1), Article 89. https://doi.org/10.59796/jcst.V15N1.2025.89
Kim, Y.-S., Kim, M. K., Fu, N., Liu, J., Wang, J., & Srebric, J. (2025). Investigating the impact of data normalization methods on predicting electricity consumption in a building using different artificial neural network models. Sustainable Cities and Society, 118, Article 105570. https://doi.org/10.1016/j.scs.2024.105570
Krishnayatra, G., Tokas, S., & Kumar, R. (2020). Numerical heat transfer analysis & predicting thermal performance of fins for a novel heat exchanger using machine learning. Case Studies in Thermal Engineering, 21, Article 100706. https://doi.org/10.1016/j.csite.2020.100706
Le, X. K., Wang, N., & Jiang, X. (2023). Nuclear mass predictions with multi-hidden-layer feedforward neural network. Nuclear Physics A, 1038, Article 122707. https://doi.org/10.1016/j.nuclphysa.2023.122707
Milan, P. J., Hickey, J. P., Wang, X., & Yang, V. (2021). Deep-learning accelerated calculation of real-fluid properties in numerical simulation of complex flowfields. Journal of Computational Physics, 444, Article 110567. https://doi.org/10.1016/j.jcp.2021.110567
Nasiri, A., Taheri-Garavand, A., Omid, M., & Carlomagno, G. M. (2019). Intelligent fault diagnosis of cooling radiator based on deep learning analysis of infrared thermal images. Applied Thermal Engineering, 163, Article 114410. https://doi.org/10.1016/j.applthermaleng.2019.114410
Navarro, H. A., & Cabezas-Gómez, L. C. (2007). Effectiveness-ntu computation with a mathematical model for cross-flow heat exchangers. Brazilian Journal of Chemical Engineering, 24(4), 509–521. https://doi.org/10.1590/S0104-66322007000400005
Nilpueng, K., Kaseethong, P., Mesgarpour, M., Shadloo, M. S., & Wongwises, S. (2022). A novel temperature prediction method without using energy equation based on physics-informed neural network (PINN): A case study on plate-circular/square pin-fin heat sinks. Engineering Analysis with Boundary Elements, 145, 404–417. https://doi.org/10.1016/j.enganabound.2022.09.032
Nogueira, É., & Nogueira, É. (2022). Efficiency and effectiveness method versus ε-NTU method with application in finned flat tube compact heat exchanger with water-ethylene glycol as nanofluid base of iron oxide nanoparticles. Journal of Materials Science and Chemical Engineering, 10(2), 1–17. https://doi.org/10.4236/MSCE.2022.102001
Ojha, V. K., Abraham, A., & Snášel, V. (2017). Metaheuristic design of feedforward neural networks: A review of two decades of research. Engineering Applications of Artificial Intelligence, 60, 97–116. https://doi.org/10.1016/j.engappai.2017.01.013
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., ... & Chintala, S. (2019). Pytorch: An imperative style, high-performance deep learning library. Advances in Neural Information Processing Systems, 32. https://doi.org/10.48550/arXiv.1912.01703
PyTorch Lightning team. (n.d.). Introducing multiple ModelCheckpoint callbacks. PyTorch Lightning Developer Blog. Retrieved from https://devblog.pytorchlightning.ai/introducing-multiple-modelcheckpoint-callbacks-e4bc13f9c185
Rodríguez-Pérez, R., & Bajorath, J. (2020). Interpretation of machine learning models using shapley values: Application to compound potency and multi-target activity predictions. Journal of Computer-aided Molecular Design, 34(10), 1013-1026. https://doi.org/10.1007/s10822-020-00314-0
Vadyala, S. R., Betgeri, S. N., & Betgeri, N. P. (2022). Physics-informed neural network method for solving one-dimensional advection equation using PyTorch. Array, 13, Article 100110. https://doi.org/10.1016/j.array.2021.100110
Wang, W., Deng, S., Zhao, D., Zhao, L., Lin, S., & Chen, M. (2020). Application of machine learning into organic Rankine cycle for prediction and optimization of thermal and exergy efficiency. Energy Conversion and Management, 210, Article 112700. https://doi.org/10.1016/j.enconman.2020.112700
Xu, Z., & Saleh, J. H. (2021). Machine learning for reliability engineering and safety applications: Review of current status and future opportunities. Reliability Engineering and System Safety, 211, Article 107530. https://doi.org/10.1016/j.ress.2021.107530
Zhang, J., Zhu, Y., Zhang, X., Ye, M., & Yang, J. (2018). Developing a long short-term memory (LSTM)-based model for predicting water table depth in agricultural areas. Journal of Hydrology, 561, 918–929. https://doi.org/10.1016/j.jhydrol.2018.04.065
Zobeiry, N., & Humfeld, K. D. (2021). A physics-informed machine learning approach for solving heat transfer equation in advanced manufacturing and engineering applications. Engineering Applications of Artificial Intelligence, 101, Article 104232. https://doi.org/10.1016/j.engappai.2021.104232
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