PD pulse sequence analysis and its relevance for on-site PD defect identification and evaluation

Abstract

Digital partial discharge (PD) diagnosis has become a state of the art, but computer aided procedures based on pattern recognition principles are negatively effected by on-site disturbances. The benefits and limits of expert diagnosis compared to machine intelligent systems are discussed. It is pointed out that a sufficient noise resistivity and test voltage independency of the diagnosis concept is a must for a successful on-site PD defect identification and evaluation. Experimental results are presented, taken into account especially novel evaluation procedures, which are more independent from the applied PD sensors. It is shown that PD defect identification on site at operating voltages and respectively disturbed data is still a challenge. A method of resolution with an intelligent noise resistant diagnosis concept is discussed. A hierarchical and redundant approach is presented, which improves the on site PD source identification as well as on-site risk assessment.