Researchers are increasingly turning to innovative control systems to optimize the performance of wind turbines, and recent work comparing fuzzy logic control (FLC) with traditional proportional integral (PI) control has yielded promising results. This comparative analysis focuses on the control mechanisms used for doubly fed induction generators (DFIG) connected to the grid, which are integral to wind energy generation.
The research, published on March 10, 2025, demonstrates the superiority of fuzzy logic controllers over enhanced PI controllers when subjected to varying wind speeds and fault conditions. The study highlights how FLC can significantly improve response times and reduce errors compared to conventional methods. This advancement is particularly relevant as the global demand for wind energy continues to rise, driven by growing population and industrial needs.
At the core of the study is the performance evaluation of FLC implemented to manage the rotor side converter of DFIGs. The authors reported substantial enhancements, with improvements ranging from 14% to 70% under normal conditions and between 40% and 70% when experiencing fault conditions. These findings suggest FLC not only offers increased efficiency but also greater reliability during turbulent operating conditions, marking it as a viable alternative to traditional control systems.
Wind energy has emerged as one of the most economic and environmentally friendly sources of renewable energy. The global wind power generating capacity is projected to reach unprecedented levels, surpassing 1 TW with 117 GW added globally as recently as 2023. To capitalize on this potential, enhancing the control systems of wind turbines is imperative.
This research emphasizes the limitations of PI controllers, particularly their inability to maintain performance during abrupt changes, such as power fluctuations due to variable wind speeds. By utilizing FLC, the study found it effectively mimics human reasoning, allowing for responsive and adaptive management of the wind turbine systems.
Simulated analyses for this study were conducted using MATLAB/Simulink, wherein various wind profiles representing seasonal changes were tested to assess how effectively each controller maintained output power. The FLC consistently outperformed the enhanced PI controller, showcasing reduced peak-to-peak oscillations by about 30% to 65% under fault conditions, leading to smoother operational performance.
The authors assert, "The FLC demonstrates significant improvement over enhanced PI control, achieving faster settling times and lower steady-state error under both normal and fault conditions." This finding holds significant importance not only for advancing wind turbine technology but also for paving the way for future applications of fuzzy logic control systems across different energy sectors. This includes potential uses for enhancing the efficiency of solar power systems and other renewable energy technologies.
Future research efforts will look to expand on these findings, potentially integrating additional parameters such as output voltage and reactive power to strive for even more extensive reforms to DFIG performance. The ability to incorporate real-time optimization is emphasized by the authors who highlight its possible advantages for improving power quality management and system reliability.
With renewable energy sources becoming increasingly important for sustainable power generation, such innovations could play a pivotal role over the coming decades as they help transition toward greener energy solutions.