Fault Detection in Transmission Line by Using IoT

The report focuses on underground cable fault detection using IoT and machine learning techniques. Underground cables play a critical role in electrical distribution systems, and timely fault detection is essential for uninterrupted power supply. The integration of IoT devices and machine learning algorithms enhances fault detection accuracy. IoT components such as sensors, communication networks, and near real-time data analytics platforms enable real-time monitoring of cable health parameters. Sensor data is analyzed by Machine learning algorithms analyze sensor data to identify patterns or anomalies indicative of cable faults. Supervised and unsupervised learning strategies, such as decision trees and support vector machines, also learning strategies, such as decision trees and support vector machines, learning strategies, such as decision trees and support vector machines, learning strategies, such as decision trees and support vector machines, clustering algorithms, are used for model training and classification of cable health states. The combined use of IoT and machine learning enables continuous monitoring, proactive fault detection, and predictive maintenance. Case studies and practical examples demonstrate the effectiveness of the approach in reducing downtime and optimizing maintenance efforts. Benefits include improved fault detection accuracy, proactive maintenance planning, and enhanced reliability of underground cable systems. The approach leads to reduced downtime, efficient resource allocation, and optimized performance of electrical distribution networks.