Research article
● Open access
FPGA-Based Optimal Fuzzy Logic Controller for Hybrid Solar-Wind Energy Systems: A Comprehensive Review and Experimental Implementation
Abstract
Background:
The global energy landscape is undergoing a profound transformation driven by the dual imperatives of sustainable development and climate change mitigation. With conventional fossil fuel reserves depleting rapidly and environmental concerns escalating, renewable energy sources have emerged as viable alternatives for meeting growing energy demands. Among these, solar photovoltaic and wind power systems represent the most mature and widely deployed technologies. However, the inherent intermittency and variability of these sources necessitate sophisticated control strategies to ensure reliable power delivery and maximum energy harvesting. Hybrid renewable energy systems combining solar and wind sources offer enhanced reliability through complementary generation patterns. In this context, the present study focuses on the design, implementation, and experimental validation of an optimal fuzzy logic controller (FLC) for hybrid solar–wind energy systems implemented on a field-programmable gate array (FPGA) platform to achieve improved tracking efficiency, faster response times, and enhanced system stability.
Methods:
The proposed hybrid system integrates photovoltaic (PV) modules and wind turbine generators with permanent magnet synchronous generators (PMSG). Mathematical modeling of both subsystems was developed using MATLAB/Simulink, incorporating detailed PV array characteristics and wind turbine aerodynamics. A Mamdani-type fuzzy logic controller was designed using error (E) and change in error (dE) as inputs with five linguistic variables (NB, NS, ZE, PS, PB). The controller determines optimal duty cycles for DC–DC converters to achieve maximum power point tracking (MPPT). For hardware implementation, the Xilinx System Generator (XSG) platform was utilized to convert Simulink models into FPGA-compatible designs. The controller was implemented on a Virtex-6 XC6VLX315T FPGA using Xilinx ISE for synthesis and bitstream generation, and system performance was evaluated under varying atmospheric conditions.
Results and Conclusion:
Experimental results demonstrated excellent performance of the proposed FPGA-based fuzzy logic controller. The system achieved maximum power point tracking efficiency of 99.7%, significantly outperforming conventional approaches. The duty cycle stabilized at 0.38 within approximately 10 ms, indicating rapid convergence to the optimal operating point. Comparative analysis showed that the XSG-based implementation achieved 5% faster power stabilization than conventional MATLAB/Simulink models. The inverter output produced clean sinusoidal waveforms with minimal harmonic distortion and accurate 120° phase separation among the three-phase voltages. Under varying wind speeds up to 15 m/s, the controller maintained stable operation with duty cycle stabilization around 0.41. These results confirm that FPGA-based fuzzy logic controllers provide superior parallel processing capability and enhanced control efficiency. The proposed system offers a promising solution for efficient integration of hybrid renewable energy sources, particularly for addressing growing electricity demands in developing countries. Future work may explore integration of deep learning and reinforcement learning techniques for improved adaptability and grid integration.
Keywords
FPGA; fuzzy logic controller; maximum power point tracking; hybrid renewable energy system; solar photovoltaic; wind energy; permanent magnet synchronous generator; Xilinx System Generator; MPPT efficiency; power electronics
References
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Rezvani, A., Izadbakhsh, M., & Gandomkar, M. (2015). Enhancement of hybrid dynamic performance using ANFIS for fast varying solar radiation and fuzzy logic controller in high speeds wind. Journal of Electrical Systems, 11(1), 11-26.
Tahir, S., Wang, J., Baloch, M. H., & Kaloi, G. S. (2018). Digital control techniques based on voltage source inverters in renewable energy applications: A review. Electronics, 7(2), 18. https://doi.org/10.3390/electronics7020018
Talaat, M., Alblawi, A., Tayseer, M., & Elkholy, M. H. (2022). FPGA control system technology for integrating the PV/wave/FC hybrid system using ANN optimized by MFO techniques. Sustainable Cities and Society, 80, 103825. https://doi.org/10.1016/j.scs.2022.103825
Abdolrasol, M. G. M., Hussain, S. M. S., Ustun, T. S., Sarker, M. R., Hannan, M. A., Mohamed, R., ... & Milad, A. (2021). Artificial neural networks based optimization techniques: A review. Electronics, 10(21), 2689. https://doi.org/10.3390/electronics10212689
Anwar, T., Memon, A. H., Baloch, M. H., & Kaloi, G. S. (2016). Fuzzy logic implementation with MATLAB for solar-wind-battery-diesel hybrid energy system. Imperial Journal of Interdisciplinary Research, 2(5), 574-584.
Baloch, M. H., Kaloi, G. S., & Memon, Z. A. (2016). Current scenario of the wind energy in Pakistan challenges and future perspectives: A case study. Energy Reports, 2, 201-210. https://doi.org/10.1016/j.egyr.2016.08.002
Baloch, M. H., Wang, J., & Kaloi, G. S. (2016). A review of the state of the art control techniques for wind energy conversion system. International Journal of Renewable Energy Research, 6(4), 1276-1295.
Belmili, H., Boulouma, S., Boualem, B., & Faycal, A. M. (2017). Optimized control and sizing of standalone PV-wind energy conversion system. Energy Procedia, 107, 76-84. https://doi.org/10.1016/j.egypro.2016.12.134
Hussain, M., Baloch, M. H., Memon, A. H., & Pathan, N. K. (2018). Maximum power tracking system based on power electronic topology for wind energy conversion system applications. Engineering, Technology & Applied Science Research, 8(5), 3392-3397. https://doi.org/10.48084/etasr.2251
Jemaa, A., Zarrad, O., Hajjaji, M. A., & Mansouri, M. N. (2018). Hardware implementation of a fuzzy logic controller for a hybrid wind-solar system in an isolated site. International Journal of Photoenergy, 2018, 1-16. https://doi.org/10.1155/2018/5379864
Joshi, A., Wazid, M., & Goudar, R. H. (2015). An efficient cryptographic scheme for text message protection against brute force and cryptanalytic attacks. Procedia Computer Science, 48, 360-366. https://doi.org/10.1016/j.procs.2015.04.194
Kaloi, G. S., Wang, J., & Baloch, M. H. (2016). Active and reactive power control of the doubly fed induction generator based on wind energy conversion system. Energy Reports, 2, 194-200. https://doi.org/10.1016/j.egyr.2016.08.001
Kumar, D., & Chatterjee, K. (2016). A review of conventional and advanced MPPT algorithms for wind energy systems. Renewable and Sustainable Energy Reviews, 55, 957-970. https://doi.org/10.1016/j.rser.2015.11.013
Memon, B., Baloch, M. H., Memon, A. H., Qazi, S. H., Haider, R., & Ishak, D. (2019). Assessment of wind power potential based on Raleigh distribution model: An experimental investigation for coastal zone. Engineering, Technology & Applied Science Research, 9(1), 3721-3725. https://doi.org/10.48084/etasr.2381
Mhmood, A. A. K., & Jumaa, F. A. (2023). A comparative study between the soft computing MPPT techniques and traditional incremental conductance under arbitrary environmental conditions. In 1st International Conference on Achieving the Sustainable Development Goals (p. 060007). https://doi.org/10.1063/5.0137308
Prasad, D., Kumar, N., Sharma, R., Malik, H., Garcia Marquez, F. P., & Pinar-Pérez, J. M. (2023). A novel ANROA based control approach for grid-tied multi-functional solar energy conversion system. Energy Reports, 9, 2044-2057. https://doi.org/10.1016/j.egyr.2023.01.039
Rezvani, A., Izadbakhsh, M., & Gandomkar, M. (2015). Enhancement of hybrid dynamic performance using ANFIS for fast varying solar radiation and fuzzy logic controller in high speeds wind. Journal of Electrical Systems, 11(1), 11-26.
Tahir, S., Wang, J., Baloch, M. H., & Kaloi, G. S. (2018). Digital control techniques based on voltage source inverters in renewable energy applications: A review. Electronics, 7(2), 18. https://doi.org/10.3390/electronics7020018
Talaat, M., Alblawi, A., Tayseer, M., & Elkholy, M. H. (2022). FPGA control system technology for integrating the PV/wave/FC hybrid system using ANN optimized by MFO techniques. Sustainable Cities and Society, 80, 103825. https://doi.org/10.1016/j.scs.2022.103825