Acoustic Fault Detection Technique for High-Power Insulators

Insulators are important equipment used to electrically isolate and mechanically hold wires in high-voltage power transmission systems. Faults caused by the deterioration of the insulators induce very serious problems to the power transmission line. Techniques were introduced for the acoustic detection of insulator faults by acoustic radiation noises. Radiation noises were measured from normal state insulators and fault state insulators in an anechoic chamber. The insulators used were two porcelain insulators, a cut-out switch, two line posters, and a lightning arrester. A new acoustic technique determines the direction of the insulator faults using source localization with three-dimensional microphone arrays. The advantage is to classify the fault state insulators without human inspection by considering the amount of total noises and 120-Hz harmonic components. The fault detection was determined by neural network to diagnose the state automatically. The proposed technique was evaluated by distinct, real datasets and the efficacy was validated. The noise source was detected with 100.0% accuracy and the classification ratio achieved 96.7% for three typical conditions.