Abstract

Maintaining power system frequency within permissible limits is one of the most critical operational challenges in modern electrical grids, particularly as the penetration of renewable energy sources continues to accelerate. Load Frequency Control (LFC) traditionally relied on conventional proportional-integral-derivative (PID) controllers, which often prove inadequate for increasingly dynamic, nonlinear, and uncertain power system environments. This paper presents an original, comprehensive review of artificial intelligence (AI)-based methodologies applied to LFC, covering artificial neural networks (ANN), fuzzy logic systems, neuro-fuzzy hybrids, reinforcement learning (RL), deep learning architectures, and meta-heuristic optimization techniques. Unlike prior surveys, this review critically evaluates each approach through the lens of contemporary multi-area grid challenges, including penetration of wind and solar generation, electric vehicle integration, energy storage systems, and deregulated market conditions. Key performance metrics, simulation environments, and real-world implementation constraints are systematically analyzed. The paper concludes by identifying open research challenges and proposing future directions, including quantum-assisted control, explainable AI for power systems, and federated learning for decentralized LFC. This review is intended to serve as a consolidated reference for researchers and practitioners in electrical engineering and smart grid technology. Keywords: Load Frequency Control; Artificial Intelligence; Fuzzy Logic; Neural Networks; Reinforcement Learning; Deep Learning; Power System Stability; Smart Grid; Renewable Energy Integration.