T. Wszolek, J. Kowal, and W. Wszolek (Poland)
corona noise, acoustic signal, power lines, diagnostics, NN computation
The paper deals with the analysis of possible application of neural networks technique to recognition of typical damages 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 damages and contamination’s. 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 damages and contamination’s of the transmission line elements have been simulated.
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