Early locating and identifying basic weak-points (sharp-edge
corona, polluted-insulator "baby arcs" and loose contact
arcing) in electrical power systems significantly decrease the
imminent failure, outage time and supply interruption. We
previously introduced a method for detecting the basic weakpoints
based on sound/waveform patterns and frequency
analysis of their ultrasonic emissions. However, nonstationary
patterns of the basic weak-points’ emitted signals
and background noise frequently led to confusing
discrimination. Therefore, this paper develops an effective
pattern recognition scheme, employing wavelet feature
extraction and Artificial Neural Network (ANN)
classification, to identify the basic weak-points and two weakpoint
combinations (polluted insulator stressed by a
transmission line with a sharp-edge and multiple sharp-edges
on the same line), based on their modulated ultrasonic
emissions. Extensive testing proved that the proposed scheme
achieved average recognition rate of 98% when tested using
weak-points underneath 33-kV and 132-kV transmission lines
with 2-second detected signals. Moreover, increasing the
acquisition time (>30 seconds) and classifying the weakpoints
based on majority voting over the ANN’s responses of
multiple (15) consecutive sections, consistently led to 100%
successful recognition of the considered weak-points.