access icon free Discrimination of three or more partial discharge sources by multi-step clustering of cumulative energy features

© The Institution of Engineering and Technology
Received 19/05/2018, Accepted 02/10/2018, Revised 25/09/2018, Published 03/10/2018

Partial discharge (PD)-based diagnosis is extensively employed in condition assessment of electrical equipment. In the case of multiple PD sources, discrimination of mixed signals is significant for reliable PD interpretation. To improve the separation performance of three or more PD sources, a multi-step discrimination method is proposed. The cumulative energy functions are exploited to characterise wave shapes of PD signals, and width and sharpness features are extracted and classified to separate mixed patterns. With the objective of maximising a novel evaluation parameter of separation capability, the oblique line and length of structure element are optimised in the feature extraction stage. For the multi-step discrimination method, the feature extraction and clustering procedures are repeatedly applied to the whole dataset, sub-classes, sub-sub-classes etc., until no more clusters are generated. To evaluate the separation performance of the proposed algorithm, a mathematical model for PD pulse is proposed, which is the multiplication of Heidler enveloping function and an oscillating function whose frequency spectrum confirms Gaussian distribution. In the end, the multi-step discrimination method is tested with PD current pulses and ultra-high-frequency signals of three artificial defects in transformer and gas-insulated system, and the results prove the effectiveness of the proposed algorithm.

Inspec keywords: partial discharges; gas insulated transformers; Gaussian distribution; pattern clustering; feature extraction

Other keywords: reliable PD interpretation; Gaussian distribution; electrical equipment; artificial defects; mathematical model; cumulative energy functions; feature extraction stage; frequency spectrum; multistep clustering; oscillating function; separation performance; sharpness features; partial discharge-based diagnosis; multistep discrimination method; cumulative energy features; multiple PD sources; width features; gas-insulated system; structure element; separation capability; PD signals; PD current pulses; mixed signals; ultrahigh-frequency signals; condition assessment; partial discharge sources; Heidler enveloping function; clustering procedures; transformer

Subjects: Gaseous insulation, breakdown and discharges; Transformers and reactors; Dielectric breakdown and discharges

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