Intelligent Fault Detection in Smart Grids Using Swarm Optimization Algorithms

Abstract

This research proposes a smart optimization approach for fault detection and system improvement in renewable energy-powered smart grids. The goal is to enhance grid performance, reliability and resilience through the application of cutting-edge artificial intelligence and swarm intelligence algorithms. This approach integrates deep learning with bio-inspired optimization techniques like Particle Swarm Optimization and Salp Swarm Optimization for fault classification, anomaly detection and system optimization. A mixed strategy is used, with real-time data from the grid fed into trained neural networks, and the optimization algorithms dynamically updating parameters to enhance decision-making and fault detection. The findings show substantial performance gains in accuracy, error reduction and efficiency, with more than 90% accuracy in fault detection and optimal energy management under dynamic operating conditions. Moreover, the system demonstrates resilience against noisy and complex grid conditions, and is scalable for use in large-scale smart grids. The research shows the combination of AI-based predictive analytics and optimization offers a scalable and effective approach to energy systems, enabling sustainable energy integration and active grid management. Efforts can be made in real-time implementation and incorporation of reinforcement learning techniques for improved performance.