On Real-Time Detection of Line Sags in Overhead Power Grids Using an IoT-Based Monitoring System: Theoretical Basis, System Implementation, and Long-Term Field Verification

For overhead power grids, unexpected serious line sagging of extra-high voltage transmission lines would easily lead to major blackouts. Different direct and indirect sag measuring methods have been proposed, but they all have their own limitations. In this study, an Internet of Things (IoT)-based sag-monitoring system is proposed, which is able to perform real-time detection of line sags. The whole system has been deployed on two 161-kV lines for long-term field testing. The effectiveness of the proposed sag-monitoring system is verified through both theoretical calculation and field measurements. In this sag-monitoring system, a sag-sensing module is equipped with an embedded triaxial accelerometer to detect line sags at different spans of a single circuit. A catenary equation that takes temperature dependency into consideration is derived, so the measured accelerometer parameters can be converted to accurate line sag values. The long-term testing results show that the proposed sag-monitoring system yields an average error of 2.09%. The average differences between the sag values coming from the proposed system and a commercial sag measuring device are relatively small (between 0.57% and 4.14%), which proves that the sag values provided by the proposed system are reliable and accurate in the long-term testing. In addition, compared to the other existing sag measuring methods, the advantages of employing the proposed system are high measurement accuracy, and enabling wide field implementation, online monitoring, long-term field operation, and real-time transmission.