Abstract
This paper introduces a new method to separate PD1 from other disturbing signals present on the high voltage genera-tors and motors. The method is based on combination of a pattern classifier, the Discrete Wavelet Transform (DWT), to de-noise PD and Time-Of-Arrival method to separate PD sources. Furthermore, it will be shown that it can recognize PD sources including rotating machine’s internal and external discharge pulses (e.g. on the bus bar).
Highlights
As a result of deterioration of insulating systems in high voltage equipments, small electrical spikes occur within the insulation [1]
Nowadays it is proved that time-frequency transforms such as Wavelet [3,4] and Hilbert–Huang transform [5] have the best performance in Partial Discharge (PD) de-noising
Applying the proposed method to recorded signals and comparing results with observations of experts showed that the method recognizes PDs with a mean error as much as 3.2% for FAR7 and 2.9% for FRR8 (FAR happens when a noise pulse is recognized as PD and FRR happens when a PD is recognized as noise.)
Summary
As a result of deterioration of insulating systems in high voltage equipments, small electrical spikes occur within the insulation [1]. This could cause further degradation of insulation and failure of the equipment. Reference [2] discusses a method based on fuzzy classification of PD. It is partially successful in recognizing cross-coupling resulted from adjacent phases, its authors admit that certainty of their method is not high. The paper is organized as follows: Section 2 clarifies problems dealt with in PD de-noising.
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