In the ever-evolving landscape of renewable energy, innovation is the wind that propels the industry forward. A groundbreaking study published recently offers a fresh gust of progress, promising to revolutionize the way we harness power from the wind. Researchers at the National Polytechnic School of Oran Maurice Audin, affiliated with the LAAS Laboratory, have developed a novel control technique that could significantly enhance the efficiency and reliability of multi-rotor wind turbine systems.
At the heart of this innovation is Habib Benbouhenni, the lead author of the study. Benbouhenni and his team have introduced a neural-modified sliding mode control approach, a sophisticated method designed to tackle one of the most persistent issues in wind energy systems: the ripples in energy and current that can lead to inefficiencies and equipment wear and tear. “The conventional approach has its merits, but it falls short in addressing these critical fluctuations,” Benbouhenni explains. “Our method aims to bridge this gap, offering a more robust and competent solution.”
The team’s approach involves integrating neural networks with sliding mode control, a technique known for its robustness in handling nonlinear systems. This hybrid method was put to the test in a double-powered induction generator-based multi-rotor wind turbine system. The results were striking. The neural-modified sliding mode control demonstrated a remarkable ability to minimize overshoot values by 99.82% for reactive power and 97.26% for active power. Moreover, it reduced current harmonic distortion and active power ripples by significant margins, as confirmed through both MATLAB simulations and hardware-in-loop experiments using dSPACE 1104.
The implications of this research are profound for the energy sector. Multi-rotor wind turbine systems are becoming increasingly popular due to their ability to capture wind energy more efficiently. However, the challenges posed by power and current fluctuations have been a significant barrier to their widespread adoption. Benbouhenni’s work offers a promising solution, potentially paving the way for more stable and efficient wind energy systems.
“The empirical results confirm the high competence and ability of the designed approach to significantly improve the quality of energy and current,” Benbouhenni states. “This makes it a reliable solution for the future of wind energy control.”
As the world continues to shift towards renewable energy sources, innovations like this one are crucial. They not only enhance the efficiency of existing technologies but also make them more commercially viable. The study, published in Scientific Reports, translates to English as Scientific Reports, underscores the potential of neural-modified sliding mode control to shape the future of wind energy. As researchers and industry experts delve deeper into this approach, we can expect to see more stable, efficient, and reliable wind turbine systems, driving the energy sector towards a greener and more sustainable future. The wind of change is blowing, and it’s carrying with it the promise of a more robust and efficient renewable energy landscape.
