Self-Learning Wind Turbine Optimization Using QuantumInspired Algorithms for Renewable Energy Efficiency
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Keywords:
Quantum-Inspired Optimization; Adaptive Machine Learning; Wind Turbine Efficiency; Fault Detection and Recovery; IoT-Enabled Data Acquisition.Abstract
Detailed considerations are required to optimize wind turbine efficiency and increase the demand for renewable energy solutions. In this research, the Quantum-Inspired Self-Learning Optimization (QISLO) framework, which combines quantum-inspired algorithms with adaptive machine learning, is proposed to enhance the performance of wind turbines. To dynamically optimize rotor blade pitch angles, yaw control, and generator torque, thereby achieving enhanced energy capture under varying wind conditions, the proposed system utilizes a Quantum-Inspired Genetic Algorithm (QIGA). Additionally, an Adaptive Reinforcement Learning (ARL) model maintains optimal control settings by modeling both historical and real-time performance of the turbine. The Quantum-Inspired Differential Evolution (QIDE) is employed to detect faults and recover them, minimizing downtime and mechanical stress to enhance the system's resilience. A Quantum-Inspired Fuzzy Logic Controller (QIFLC) is used to maintain turbine operations within a controlled range when operating in turbulent conditions. Combined with the integration of an IoT-enabled data acquisition system, this gives real-time environmental data. The experimental results of QISLO also demonstrate a 20-30% improvement in energy efficiency and a 40% reduction in mechanical wear compared to conventional optimization techniques. It is an innovative solution to the limitations of traditional turbine control systems, offering increased reliability of energy production, enhanced wind farm sustainability, and improved overall operational efficiency. Future work will also scale the system to large-scale offshore wind farms and grid integration.
References
Perry, S., Klemeš, J., & Bulatov, I. (2008). Integrating waste and renewable energy to reduce the carbon footprint of locally integrated energy sec-tors. Energy, 33(10), 1489-1497. https://doi.org/10.1016/j.energy.2008.03.008.
Chandra Das, K. (2019). Library Orientation Programme in School Libraries: Awareness to Students and Teachers. Indian Journal of Information Sources and Services, 9(2), 5–9. https://doi.org/10.51983/ijiss.2019.9.2.634.
Leith, D. J., & Leithead, W. E. (1997). Implementation of wind turbine controllers. International Journal of Control, 66(3), 349-380. https://doi.org/10.1080/002071797224621.
Wall, A., &Agrafiotis, I. (2021). A Bayesian approach to insider threat detection. Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications, 12(2), 48-84.
Gharehchopogh, F. S. (2023). Quantum-inspired metaheuristic algorithms: comprehensive survey and classification. Artificial Intelligence Re-view, 56(6), 5479-5543. https://doi.org/10.1007/s10462-022-10280-8.
View more references (24)
Hossein Foruzesh. (2016). Study of the causes of deterioration and new methods of demolition of concrete and metal structures. International Aca-demic Journal of Science and Engineering, 3(1), 11–22.
Apata, O., &Oyedokun, D. T. O. (2020). An overview of control techniques for wind turbine systems. Scientific African, 10, e00566. https://doi.org/10.1016/j.sciaf.2020.e00566.
Saç, G., &Özuluğ, M. (2021). Length-weight and length-length relationships of three endemic freshwater fish species from Anatolia, Turkey. Natural and Engineering Sciences, 6(1), 53-59. https://doi.org/10.28978/nesciences.868080.
Boaro, M., Fuselli, D., Angelis, F. D., Liu, D., Wei, Q., & Piazza, F. (2013). Adaptive dynamic programming algorithm for renewable energy sched-uling and battery management. Cognitive Computation, 5, 264-277. https://doi.org/10.1007/s12559-012-9191-y.
Marinković, G., Lazić, J., Grgić, I., & Ilić, Z. (2018). Application of Copras Method for Land Consolidation Projects Ranking. Archives for Technical Sciences, 2(19), 35-44. https://doi.org/10.7251/afts.2018.1019.035M.
Khan, S. Z., Muzammil, N., Ghafoor, S., Khan, H., Zaidi, S. M. H., Aljohani, A. J., & Aziz, I. (2024). Quantum long short-term memory (QLSTM) vs. classical LSTM in time series forecasting: a comparative study in solar power forecasting. Frontiers in Physics, 12, 1439180. https://doi.org/10.3389/fphy.2024.1439180.
Anand, R. V., Magesh, G., Alagiri, I., Brahmam, M. G., Balusamy, B., & Benedetto, F. (2024). Quantum Channel Optimization: Integrating Quan-tum-Inspired Machine Learning with Genetic Adaptive Strategies. IEEE Access.
Liao, Y., Qin, G., & Liu, F. (2020). Particle swarm optimization research based on quantum self-learning behavior. Journal of Computational Methods in Science and Engineering, 20(1), 91-99. https://doi.org/10.3233/JCM-193644.
Hansen, M. H., Hansen, A., Larsen, T. J., Øye, S., Sørensen, P., & Fuglsang, P. (2005). Control design for a pitch-regulated, variable speed wind tur-bine.
Zhang, F., Lu, J., Yang, S., Liu, W., Tao, R., Zhu, D., & Xiao, R. (2023). Performance improvement of a pump as turbine in storage mode by optimi-zation design based on genetic algorithm and fuzzy logic. Journal of Energy Storage, 62, 106875. https://doi.org/10.1016/j.est.2023.106875.
Roy, P., He, J., Zhao, T., & Singh, Y. V. (2022). Recent advances of wind-solar hybrid renewable energy systems for power generation: A re-view. IEEE Open Journal of the Industrial Electronics Society, 3, 81-104. https://doi.org/10.1109/OJIES.2022.3144093.
Rahim, S., Javaid, N., Ahmad, A., Khan, S. A., Khan, Z. A., Alrajeh, N., & Qasim, U. (2016). Exploiting heuristic algorithms to utilize energy man-agement controllers with renewable energy sources efficiently. Energy and Buildings, 129, 452-470. https://doi.org/10.1016/j.enbuild.2016.08.008.
Wang, S., Yue, Y., Cai, S., Li, X., Chen, C., Zhao, H., & Li, T. (2024). A comprehensive survey of the application of swarm intelligent optimization algorithm in photovoltaic energy storage systems. Scientific Reports, 14(1), 17958. https://doi.org/10.1038/s41598-024-68964-w.
Manthila, P., & Madugalla, K. A. (2025). Hybrid energy storage systems for grid-interactive renewable power: Design, control, and reliability perspec-tives. National Journal of Renewable Energy Systems and Innovation, 1(4), 34–42.
Kavitha, M. (2025). Hybrid forecasting of wind and solar power using transformer-based deep learning models. National Journal of Intelligent Power Systems and Technology, 1(2), 1–9.
Sikalu, T. C., & Kuma, R. (2025). AI, IoT, and blockchain integration for renewable energy systems: Toward intelligent, secure, and resilient energy infrastructures. National Journal of Electric Drives and Control Systems, 1(3), 17–23.
Al-Jame, F., & Btia, J. (2025). AI-driven control strategies for power converters in solar–wind hybrid energy systems: Enhancing efficiency, reliability, and grid resilience. Transactions on Power Electronics and Renewable Energy Systems, 1(3), 1–7.
Sikalu, K. P., & Kantor, K. N. (2025). Enabling energy storage in emerging economies: Policy pathways for accelerated adoption. Transactions on Energy Storage Systems and Innovation, 1(3), 1–9.
Muralidharan, J., & Abdullah, D. (2025). Sensorless control techniques for high-speed electrical machines using machine learning-assisted observers. National Journal of Electrical Machines & Power Conversion, 1(2), 16–23.
Wilamowski, G. J. (2025). Embedded system architectures optimization for high performance edge computing. SCCTS Journal of Embedded Systems Design and Applications, 2(2), 47–55.
Luedke, R. H., & Prashanth, R. (2025). Design and implementation of edge-enabled IoT framework for real-time environmental monitoring. Journal of Wireless Sensor Networks and IoT, 3(1), 18–24.
Wang, L., Dong, Q., Fu, Y., Zhang, B., Chen, M., Xie, J., ... & Luo, Z. (2024). Effectiveness of cooperative yaw control based on reinforcement learning for in-line multiple wind turbines. Control Engineering Practice, 153, 106124. https://doi.org/10.1016/j.conengprac.2024.106124.
Huang, Y., Lin, S., & Zhao, X. (2023). Multi-agent reinforcement learning control of a hydrostatic wind turbine-based farm. IEEE Transactions on Sustainable Energy, 14(4), 2406-2416. https://doi.org/10.1109/TSTE.2023.3270761.
Lee, C. Y., &Maceren, E. D. C. (2025). Physics‐informed anomaly and fault detection for wind energy systems using deep CNN and adaptive elite PSO‐XGBoost. IET Generation, Transmission & Distribution, 19(1), e13289. https://doi.org/10.1049/gtd2.13289.