Research Article
Girase Sagar Mahendrasing · Prof T. Y. Kharche
Journal
International Journal of Digital Applications and Contemporary Research (IJDACR)
ISSN
2319-4863
Volume / Issue
Vol.13 · Issue 10
Published
May 2025
Access
Open Access
Licence
CC BY-NC-SA 4.0
This paper presents a hybrid control strategy combining Particle Swarm Optimization (PSO) with Neural Networks (NN) to enhance the stability of the Single Machine Infinite Bus (SMIB) system. Conventional Power System Stabilizers (PSS) are effective in suppressing electromechanical oscillations but struggle with the dynamic and non-linear complexities of modern power systems. The proposed PSO-NN controller automatically tunes the neural network parameters, leveraging the global search capabilities of PSO to optimize system stability under varying conditions. Simulation results demonstrate significant improvements in transient stability, reduced oscillations, and faster settling times, particularly in minimizing rotor angle error and speed overshoot. This approach offers a robust solution for modern interconnected grids, addressing increasing system complexities and disturbances. The study also suggests potential extensions, such as incorporating renewable energy sources and exploring additional optimization algorithms to further enhance grid resilience and stability.
Girase Sagar Mahendrasing, Prof T. Y. Kharche (2025). Enhancing Power System Stability Integrating Neural Network Control & PSO Optimization for Single Machine Infinite Bus. International Journal of Digital Applications and Contemporary Research (IJDACR), Vol.13, Issue 10. ISSN: 2319-4863.
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