Optimized ANFIS-Based Control of Variable-Speed Wind Turbines Using Bayesian Optimization for Enhanced Efficiency and Adaptability

Bagher Khademhamedani, Masoud Izadi, Hadi Delavar

Abstract


Variable-speed wind turbines are important for achieving better energy efficiency and reducing mechanical wear in changing wind conditions. This research improves the Adaptive Neuro-Fuzzy Inference System (ANFIS) used for controlling these turbines by proposing a new approach for tuning hyperparameters with Bayesian Optimization. The method searches the hyperparameter space dynamically, helping to optimize fuzzy membership functions and training parameters. This improves the model’s accuracy while also lowering computational effort. Simulations in MATLAB show that the optimized ANFIS achieves more power output and smoother control compared to traditional methods under varying wind speeds. Results indicate that turbine efficiency is significantly increased, with a 15% reduction in mean squared error (MSE), and the system adapts better to real-time changes in wind conditions. These findings highlight the potential of combining Bayesian Optimization with ANFIS for improving wind energy systems.


Keywords


Wind Turbine Control, Adaptive Neuro-Fuzzy Inference System, Bayesian Optimization, Renewable Energy

Full Text:

Abstract PDF

References


Al Hadi, F. M., & Aly, H. H. (2024). Harmonics Forecasting of Renewable Energy System using Hybrid Model based on LSTM and ANFIS. IEEE Access.

Badihi, H., Zhang, Y., Pillay, P., & Rakheja, S. (2020). Fault-tolerant individual pitch control for load mitigation in wind turbines with actuator faults. IEEE Transactions on Industrial Electronics, 68(1), 532-543.

Beltran, B., Ahmed-Ali, T., & Benbouzid, M. E. H. (2008). Sliding mode power control of variable-speed wind energy conversion systems. IEEE Transactions on energy conversion, 23(2), 551-558.

Bowermaster, D., Alexander, M., & Duvall, M. (2017). The Need for Charging: Evaluating utility infrastructures for electric vehicles while providing customer support. IEEE Electrification Magazine, 5(1), 59-67.

Cahyadi, B. N., Khatami, M., Zulfatman, Z., Irfan, M., & Waliyuddin, A. B. (2024). Blade pitch angle control of wind turbine based ANFIS controller. AIP Conference Proceedings,

Candade, A. A., Ranneberg, M., & Schmehl, R. (2020). Structural analysis and optimization of a tethered swept wing for airborne wind energy generation. Wind Energy, 23(4), 1006-1025.

Dubey, A., & Santoso, S. (2015). Electric vehicle charging on residential distribution systems: Impacts and mitigations. IEEE Access, 3, 1871-1893.

Fazlollahi, V., Taghizadeh, M., & A Shirazi, F. (2019). ANFIS modeling and validation of a variable speed wind turbine based on actual data. Energy Equipment and Systems, 7(3), 249-262.

Ghobakhloo, M., Iranmanesh, M., Mubarak, M. F., Mubarik, M., Rejeb, A., & Nilashi, M. (2022). Identifying industry 5.0 contributions to sustainable development: A strategy roadmap for delivering sustainability values. Sustainable Production and Consumption, 33, 716-737.

Guerra, M. I., de Araújo, F. M., de Carvalho Neto, J. T., & Vieira, R. G. (2024). Survey on adaptative neural fuzzy inference system (ANFIS) architecture applied to photovoltaic systems. Energy Systems, 15(2), 505-541.

Haddadi, E., Zimnoch, M., & Tabarraei, A. (2023). Determination of Material Parameters of In740H Under Different Experimental Situations Using Chaboche Model. ASME International Mechanical Engineering Congress and Exposition,

Holl, M., Platzer, M., & Pelz, P. (2015). Optimal energy systems design applied to an innovative ocean–wind energy converter. WIT Transactions on Ecology and the Environment, 193, 547-557.

Izadi, M., Jabari, M., Izadi, N., Jabari, M., & Ghaffari, A. (2021). Adaptive Control based on the Lyapunov Reference Model Method of Humanoid Robot Arms using EFK. 2021 13th Iranian Conference on Electrical Engineering and Computer Science (ICEESC),

Izadi, N., Jabari, M., Izadi, M., Jabari, M., & Ghaffari, A. (2022). Optimal Path Design for a Flexible Rigid Two-Bar Robot in Point-to-Point Motion. 2022 14th Iranian Conference on Electrical Engineering and Computer Science (ICEESC),

Izadi, S., Izadi, M., Jabari, K., & Zaker, B. (2024). Enhancing Efficiency in Bidirectional DC-DC Converters through PSO-Based Optimization. 2024 9th International Conference on Technology and Energy Management (ICTEM),

Izadi, S., Jabari, K., Izadi, M., Hamedani, B. K., & Ghaffari, A. (2021). Identification and Diagnosis of Dynamic and Static Misalignment in Induction Motor Using Unscented Kalman Filter. 2021 13th Iranian Conference on Electrical Engineering and Computer Science (ICEESC),

Jabari, K., Izadi, M., Izadi, S., Hamedani, B. K., & Ghaffari, A. (2022). Predictive and Data-Driven Control of Traffic Lights in Urban Road Networks using Linear and Time-Varying Model. 2022 14th Iranian Conference on Electrical Engineering and Computer Science (ICEESC),

Jakobsen, U., Lu, K., Rasmussen, P. O., Lee, D.-H., & Ahn, J.-W. (2014). Sensorless control of low-cost single-phase hybrid switched reluctance motor drive. IEEE Transactions on Industry Applications, 51(3), 2381-2387.

Khademhamedani, B., Izadi, M., Izadi, N., Azarshab, A., Najari, A., & Zaker, B. (2023). Transformative Control Optimization in PMSG-Based Wind Energy Systems: A Deep Reinforcement Learning Approach. 2023 3rd International Conference on Electrical Machines and Drives (ICEMD),

Khademhamedani, B., Izadi, N., Izadi, M., & Najar, A. (2024). Enhancing Power System Stability through Solar Farm-Based Damping of Subsynchronous Oscillations. 2024 9th International Conference on Technology and Energy Management (ICTEM),

Li, J., & Li, W. (2020). On-line PID parameters optimization control for wind power generation system based on genetic algorithm. IEEE Access, 8, 137094-137100.

Mohamed, A., Salehi, V., Ma, T., & Mohammed, O. (2013). Real-time energy management algorithm for plug-in hybrid electric vehicle charging parks involving sustainable energy. IEEE Transactions on Sustainable Energy, 5(2), 577-586.

Nilashi, M., bin Ibrahim, O., Ithnin, N., & Sarmin, N. H. (2015). A multi-criteria collaborative filtering recommender system for the tourism domain using Expectation Maximization (EM) and PCA–ANFIS. Electronic Commerce Research and Applications, 14(6), 542-562.

Qiu, W., Zhao, X., Tyrrell, A., Perinpanayagam, S., Niu, S., & Wen, G. (2024). Application of Artificial Intelligence-Based Technique in Electric Motors: A Review. IEEE Transactions on Power Electronics.

Suhail, M., Akhtar, I., Kirmani, S., & Jameel, M. (2021). Development of progressive fuzzy logic and ANFIS control for energy management of plug-in hybrid electric vehicle. IEEE Access, 9, 62219-62231.

Turker, H., & Bacha, S. (2018). Optimal minimization of plug-in electric vehicle charging cost with vehicle-to-home and vehicle-to-grid concepts. IEEE Transactions on Vehicular Technology, 67(11), 10281-10292.

Wappelhorst, S. (2020). The end of the road? An overview of combustion engine car phase-out announcements across Europe.

Xu, B. (2024). Financial decentralization, renewable energy technologies, energy subsidies and wind power development in China: An analysis of nonparametric model. Journal of Cleaner Production, 434, 139902.


Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.