Adaptive Protection Based Fault Detection, Classification, and Location in Power System

Modern power systems require advanced techniques to detect, classify, and locate faults efficiently. This study explores the use of Adaptive Neuro-Fuzzy Inference System (ANFIS) for fault detection location identification and type of fault (Symmetrical and unsymmetrical) faults. ANFIS, combining neural networks and fuzzy logic, can handle the complexities and uncertainties of power system faults. By training on historical data, ANFIS accurately identifies different fault types and pinpoints their locations. Simulation results show its effectiveness in terms of accuracy and speed, making it a valuable tool for improving power system reliability and reducing downtime. Future work will focus on real-time implementation and integration with other smart grid technologies to further enhance fault management capabilities.