Recognition of defects in high voltage transmission lines using the acoustic signal of corona effect

The paper deals with the analysis of the possible application of neural networks to the recognition of typical damage of UHV transmission lines. The acoustic signal generated as a result of corona effects is used as a damage symptom, as its intensity is usually increased after damage occurrence or after contamination of the surface of a conductor or an insulator string. The primary problem in the diagnostic process is the distinguishing between signals generated as results of damage and contamination. The problem is not solved by methods based on the RF signal interference or by the classical methods of acoustic signal analysis. The construction and verification of the assumed diagnostic model have been carried out by experimental studies in laboratory conditions, where typical damage and contamination of the transmission line elements have been simulated.