| Abstract: |
Modern power grids face unprecedented challenges due to increasing complexity, renewable energy integration, and distributed generation systems. This research investigates the application of artificial intelligence techniques for fault analysis and protective control mechanisms in contemporary electrical networks. The study examines machine learning algorithms, deep learning models, and hybrid AI approaches for real-time fault detection, classification, and isolation in smart grids. The primary objective is to evaluate the effectiveness of AI-enabled protective systems compared to conventional relay-based protection schemes. A comprehensive methodology involving simulation-based analysis and performance evaluation of various AI techniques was employed. The hypothesis posits that AI-enabled systems demonstrate superior accuracy, faster response times, and enhanced adaptability in fault scenarios. Results indicate that deep learning models achieve 97.8% accuracy in fault classification, while hybrid approaches reduce detection time by 68% compared to traditional methods. Statistical analysis reveals significant improvements in sensitivity, selectivity, and reliability metrics. The discussion emphasizes the transformative potential of AI in enhancing grid resilience and operational efficiency. This study concludes that AI-enabled protective control systems represent a paradigm shift in power system protection, offering substantial improvements in fault management capabilities. |
