Classification of common partial discharge types in oil-paper insulation system using acoustic signals

Abstract

This paper addresses classifying different common partial discharge (PD) types under different acoustic emission (AE) measurement conditions. Four types of PDs are considered for the multi-class classification problem, namely; PD from a sharp point to ground plane, surface discharge, PD from a void in the insulation, and PD from semi parallel planes. The collected AE signals are processed using pattern classification techniques to identify their corresponding PD types. The measurement conditions include the influences of various PD locations, oil temperatures, and having a barrier in the line-of-sight between the PD source and the AE sensor. A recognition rate of 94% is achieved when classifying the different PD types measured at the same conditions. In addition, it has been found that the different PD source locations, oil temperatures, and barrier insertion have an impact on the recognition rate. However, by including AE samples at these different conditions in the training process, a recognition rate around 90% for all cases is achieved.

Classification of Partial Discharge Measured under Different Levels of Noise Contamination

“…This is done without compromising data information in the reduced space, with only minimal information loss. This is achieved by projecting data at a direction with the biggest variance at a lower dimension that will maximize the scatter of the projected samples [54]. This linear subspace is found by solving an Eigen problem, where cov ( X ) is the covariance matrix of the dataset X , M is a linear mapping created by the d principle eigenvectors of the covariance matrix and λ are the d principal eigenvalues.…”

Promoting condition monitoring and diagnostics of electrical insulation in undergraduate capstone graduation projects

“…Automatic classification of the PD type (surface, sharp, void) was achieved using a typical artificial neural network model [6]. This model was trained using approximately 800 of the 1,000 spectra, and the rest were used to test the validity of the artificial neural network.…”

“…Laboratory partial discharge setup:(1) and(2) 40-kV high-voltage testing unit, (3) oil-filled tank, (4) high-voltage electrode, (5) Hilbert fractal antenna,(6) high-frequency amplifier, (7) spectrum analyzer, (8) data acquisition system, and (9) DC supply.…”

Design of Partial Discharge Test Environment for Oil-Filled Submarine Cable Terminals and Ultrasonic Monitoring

“…In electrical equipment with a solid-liquid composite medium constituting the main body, PD is also caused by a complex structure and an uneven electric field distribution. During PD and other deterioration of insulation, electric pulses, gas products, ultrasonic and electromagnetic radiation, light, local overheating, and energy loss often occur [3][4][5]. Ultrasonic signal detection can be used to effectively monitor the PD of insulation.…”