Electricity Fault Detection using AI/Ml

I Power stations require regular stability. To restore stability, an interruption such as an inaccuracy must be confined. This paper provides an in-depth examination of fault detection methods for transmission systems for electricity. However, the problem of fault detection and classification in transmission systems remains an open challenge due to the manual, time-consuming process that requires appropriate domain knowledge and experience in fault diagnosis. For a more logical and comprehensive understanding of the concept, switch ID, wire size, resistance, length, No. of joint samples, Error detection, classification, and location are typically shown separately. Artificial intelligence (AI) and machine learning techniques are primarily used in fault diagnosis and clustering techniques. This paper proposes using machine learning techniques to detect and predict fault types and where more loads and faults are likely to occur in transmission lines. K means clustering and sklearn concepts are implemented to identify and detect power line faults. Simulation results confirm the efficiency of the proposed model for troubleshooting. A comprehensive comparison of the simulation results with previous studies demonstrates the measures taken to minimize power losses.