Digital Image Processing for On-line Classification of Partial Discharge Patterns in Electrical Equipment

This paper presents a new method to analyze partial discharge (PD) digital patterns obtained from power equipment measurements to automatically classify PD patterns than can issue alarms in continuous monitoring systems. The method has two steps, in the firs one, a preclassification analyses the information contained in an *.dat type file and it generates a JPG image of the PD pattern. Then a color to grey reduction is performed to account for random variations and sporadic pulses are removed to discriminate sporadic activity and background interference. The second step involves the use of graphical filters and morphological operations like image opening and closing to segment and define regions of interest. Finally, a contour following technique is applied with Gaussian regression to identify corona from partial discharges. Obtained results include arrow pointing images with text labels that identify different PD types. This feature will greatly improve on-line monitoring system diagnosis.