Vellore Tech’s Breakthrough: Fast Terminal Sliding Mode Controller for Grid Stability

In the ever-evolving landscape of the energy sector, power quality remains a critical concern, particularly with the increasing integration of renewable energy sources and non-linear loads into the grid. A recent study published in the “International Journal of Mathematical, Engineering, and Management Sciences” offers a promising solution to mitigate voltage quality issues, potentially revolutionizing how industries approach power stability.

The research, led by Jeevan J. Inamdar from the School of Electrical Engineering at Vellore Institute of Technology in Chennai, India, focuses on the dynamic voltage restorer (DVR), a device designed to inject necessary voltage levels during faults to maintain load side voltage within specified boundaries. The study addresses the limitations of classical sliding mode control, which often takes a long time to minimize errors, by introducing a fast terminal sliding mode controller enhanced with a nonlinear quantity.

“Our goal was to improve the accuracy and durability of the conventional algorithm,” Inamdar explains. “By integrating a nonlinear quantity, we developed a fast terminal sliding mode controller that significantly enhances the performance of the DVR.”

The study further combines this methodology with an artificial neural network to improve the performance of a double feeder power system under nonlinear load conditions. The system was modeled and simulated in a MATLAB/Simulink environment, employing various optimization techniques such as particle swarm optimization, genetic algorithm, and mind blast algorithm, followed by nonlinear controllers.

The research team simulated faults to mimic voltage sags A, E, and B under different load conditions, including linear, dynamic, and nonlinear loads. They assessed the compensation percentage using power quality indices such as total harmonic distortion, harmonic compensation ratio, sag score, and voltage sag lost energy index.

The results were impressive. The sag score improved by 80%, increasing the voltage sag lost energy index to more than 95%. “Our quantitative data demonstrate the efficacy of the proposed method in mitigating voltage sag while simultaneously reducing grid voltage imbalance and distortion, irrespective of the fault type,” Inamdar notes.

The implications for the energy sector are substantial. Improved power quality can lead to lower operational costs, reduced maintenance expenses, and enhanced equipment longevity. As industries increasingly rely on sensitive electronic equipment and renewable energy sources, the need for robust power quality solutions becomes ever more critical.

This research not only addresses immediate concerns but also paves the way for future developments in power quality management. The integration of advanced control algorithms and artificial neural networks could set a new standard for maintaining stable and efficient power systems.

As the energy sector continues to evolve, the work of Inamdar and his team offers a glimpse into the future of power quality management, promising a more stable and efficient energy landscape for industries worldwide.

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