Abstract
This paper compared the capabilities of the artificial neural network (ANN) and the fuzzy logic (FL) approaches for recognizing and discriminating partial discharge (PD) fault classes. The training and testing parameters for the ANN and FL comprise statistical fingerprints from different phase-amplitude-number (φ-q-n) measurements. Two PD fault classes considered are internal discharges in voids and surface discharges. In the void class, there are single voids, serial voids and parallel voids in polyethylene terephthalate (PET), while the surface discharge class comprises four different surface discharge arrangements on pressboard in oil at different voltages and angular positioning of the ground electrode on the respective pressboards. Previously, the ANN and FL have been investigated for PD classification, but there is no work reported in the literature that compares their performance, specifically when applied for real time PD detection problem. As expected, both the ANN and FL can recognize PD defect classes, but the results show that the ANN appears to be more robust as compared to the FL, but these conclusions required to be further investigated with complex PD examples. Finally, both the ANN and FL were assessed as practical PD classification. Despite of the limitations of the ANN, it is concluded that the ANN is better suited for practical PD recognition because of its ability to provide accurate recognition values and the severity level of PD defects.
Highlights
One technique for examining failures in the insulation of high-voltage (HV) equipment is through the monitoring evaluation of partial discharges (PDs)
Experiments were carried out and PD samples captured over a 7 h stressing period in order to obtain a fair representative of the PD
Statistical features obtained from the pulse height and pulse count distributions were applied as input to the artificial neural network (ANN) and fuzzy logic (FL) systems
Summary
One technique for examining failures in the insulation of high-voltage (HV) equipment is through the monitoring evaluation of partial discharges (PDs). The nature and characteristics of PD have so far been established and evaluated [2,5], but what remains of interest nowadays is identifying enhanced techniques that can effectively identify and discriminate different PD sources and the noise associated with them such as voids in electrical insulation materials, surface discharges and corona [6,7,8]. This is important in order to provide a reliable assessment of the HV insulation condition and the nature of the PD fault [9,10]. To investigate whether any pattern recognition tool can capture slight variations of Experimental similar PD fault types
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