High Accuracy Intelligent Sag Detection and Alert System for Overhead Power Lines with IoT Support

Abstract

Aim: This work aims to develop a rapid and smart Edge-based alert system to detect sag and voltage faults in overhead power lines and send timely alerts. Materials and Methods: The system uses edge computing and time-based threshold validation to provide immediate warnings with cloud logging and alert SMS through a GSM module. Group 1: In the previous proposed method using Centralized KNN-based detection, sag sensors were utilized to sense the environment and the three-axis acceleration, whose values were then used to calculate the sag angle. The system to achieve 96% accuracy with transmit signal within 15 seconds detected the voltage fluctuation. Group 2: The ESP32 system with HC-SR04, ZMPT101B, SIM800L and CAAV2596 as well as SVM based edge processing supports high-speed local classification which can realize the detection time of 5 seconds, also classification accuracy of 98% and a lower processing latency compared with the traditional centralized monitoring method. Results: The developed system demonstrated Overall accuracy of 98%. The GSM warning messages were successfully transmitted in 5 seconds after identifying the irregular conditions. Conclusion: The experimental observations confirm the proposed IoT-based transmission line sag monitoring system provides reliable, accurate and real-time sag detection approaches.