Advanced monitoring system for integrity assessment of electric power transmission lines

An advanced monitoring system, composed of an electromagnetic acoustic transducer (EMAT), and a neural-based classifier has been developed. The fault detection is achieved by analyzing the reflected displacement waves from broken strands of the transmission line. This monitoring system can assess the state of an energized electric transmission lines in real-time. For pattern analysis of reflected signals and extracting important features from the raw data, signal processing and feature extraction techniques are employed. In addition, this paper presents a technique for synthesizing signatures using neural-fuzzy inference engine for enhancing the robustness of the classifier. Experimental results verifying performance of this new monitoring system are presented.