Automatic Detection of Long-Term Audible Noise Indices from Corona Phenomena on UHV AC Power Lines

One of the most important tasks in outdoor acoustic monitoring stations is automatic extraction of the measured signal parameters. In case of corona discharge noise from ultra-high voltage alternating current (UHV AC) power lines it is necessary to select properly the corona audible noise (CAN) parameters to be monitored for noise indicators calculation, as the monitored signal and the background noise strongly fluctuate. A combined selection of distinctive features of CAN is necessary in order to distinguish the actual signal from the external interference. The vast amount of recorded data is difficult to store and process. Therefore, particular attention was devoted to define of the collected parameters used for automatic calculation of the CAN long-term noise indicators. In addition, several new CAN parameters were introduced, including spectral moments, spectral coefficients of tonal components contribution, and power coefficients in selected frequency bands; as it allowed more efficient selection of samples with non-zero contribution from CAN. The artificial neural network was applied for final classification of the measured samples. Selected and properly filtered samples provided the basis for calculations of long-term noise indicators. Efficiency of the said method was tested for the measurement sections with the recorded sound signal and aural qualification of the CAN intensity.