Authors: Dr. Sneha R. Menon, Dr. Subrata K. Dey
Abstract: The increasing complexity of modern electrical networks, including smart grids, renewable energy integration, and distributed generation, has necessitated the adoption of intelligent optimization strategies. Artificial
Intelligence (AI) techniques, such as machine learning (ML), evolutionary algorithms, and deep reinforcement learning, provide powerful tools for optimizing electrical network design. This paper explores AI-based optimization methods for electrical networks, covering load balancing, energy efficiency, fault management, and system reliability. The study discusses AI techniques applied to network topology, component sizing, voltage control, and power flow management. Challenges such as data quality, computational complexity, and model interpretability are addressed. Tables summarize AI algorithm comparisons and network performance metrics, while a 2D diagram illustrates a typical AI-optimized network design framework.
Keywords: Electrical network design, AI optimization, Smart grids, Machine learning, Evolutionary algorithms, Power system reliability, Renewable integration.
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