In a significant stride towards enhancing the efficiency of small-scale hydropower systems, researchers have developed a novel approach to optimize load frequency control. This innovative method, spearheaded by Ghanshyam G. Tejani from the Department of Mechanical Engineering at the University of GSFC in Vadodara, Gujarat, India, leverages Type-II fuzzy logic and deep learning techniques to maintain stable frequency levels in small hydropower plants.
The study, published in the Journal of Artificial Intelligence and System Modelling (translated to English as ‘Journal of Artificial Intelligence and System Modeling’), addresses a critical challenge in small hydropower systems: dynamic frequency variations. Unlike larger plants, small hydropower systems often lack dump loads or other mechanisms to control frequency, leading to fluctuations that can impact system stability and efficiency.
Tejani and his team propose a Fuzzy Type-II-Proportional-Derivative (Fuzzy Type-II-PD) controller to tackle this issue. This controller uses fuzzy rules to optimize proportional-derivative controllers, which are further enhanced by deep learning techniques, specifically Spiking Neural Networks. The result is a Deep Spiking Neural Network that significantly improves frequency control performance.
“The proposed schema has robust and high-performance frequency control in comparison to other methods,” Tejani stated, highlighting the effectiveness of their approach. The simulation results, conducted using MATLAB/Simulink, demonstrate the controller’s ability to maintain frequency at a nominal value under dynamic conditions.
For the maritime sector, this research opens up new avenues for integrating small hydropower systems into microgrids, particularly in remote or island locations where diesel generators are currently used. The proposed controller can be adapted for diesel generators as well, offering a more efficient and sustainable solution. Additionally, the ability to turn off diesel generators when consumer demand is higher than electricity generation can lead to significant cost savings and reduced environmental impact.
“This technique can be used in different designations for both diesel generators and mini-hydropower systems at low stream flow,” Tejani explained, underscoring the versatility of their approach. The integration of small hydropower systems into maritime microgrids can enhance energy resilience and reduce reliance on fossil fuels, aligning with global sustainability goals.
The commercial impacts of this research are substantial. Improved frequency control in small hydropower systems can lead to more stable and reliable power supply, reducing downtime and maintenance costs. Moreover, the integration of deep learning techniques offers opportunities for further optimization and automation, paving the way for smarter and more efficient energy management systems.
As the maritime industry continues to seek sustainable and efficient energy solutions, this research provides a promising pathway for leveraging small hydropower systems. The proposed controller not only enhances the performance of hydropower plants but also offers a flexible solution that can be adapted to various energy systems, including diesel generators. This versatility makes it a valuable tool for maritime professionals looking to optimize their energy infrastructure and reduce their environmental footprint.