Advanced Soft Computing Techniques for Enhancing Power System Performance and Optimization

The increasing complexity and dynamic nature of modern power systems demand advanced optimization techniques to ensure reliable, efficient, and cost-effective operation. This paper explores the development and application of soft computing techniques, including fuzzy logic, genetic algorithms, and artificial neural networks, for enhancing power system performance and optimization. By integrating these intelligent computational approaches, the study addresses challenges such as load balancing, economic dispatch, voltage stability, and fault management. Simulation results demonstrate that soft computing-based methods outperform conventional optimization techniques in terms of accuracy, convergence speed, and adaptability to changing system conditions. The proposed framework provides a robust and flexible solution for real-time power system optimization, contributing to improved operational efficiency and sustainability.

Soft Computing, Power System Optimization, Fuzzy Logic, Genetic Algorithms, Artificial Neural Networks, Load Balancing, Voltage Stability, Economic Dispatch

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