Novel Features and PRPD Image Denoising Method for Improved Single-Source Partial Discharges Classification in On-Line Hydro-Generators

Ramon C. F. Araújo, Rodrigo M. S. de Oliveira, Fernando S. Brasil and Fabrício J. B. Barros
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Ramon C. F. Araújo: Electrical Engineering Graduate Program, Federal University of Pará, Belém 66075-110, Brazil
Rodrigo M. S. de Oliveira: Electrical Engineering Graduate Program, Federal University of Pará, Belém 66075-110, Brazil
Fernando S. Brasil: Eletrobras Eletronorte, Rod. Arthur Bernardes n° 2175, Belém 66115-000, Brazil
Fabrício J. B. Barros: Electrical Engineering Graduate Program, Federal University of Pará, Belém 66075-110, Brazil

Energies, 2021, vol. 14, issue 11, 1-33

Abstract: In this paper, a novel image denoising algorithm and novel input features are proposed. The algorithm is applied to phase-resolved partial discharge (PRPD) diagrams with a single dominant partial discharge (PD) source, preparing them for automatic artificial-intelligence-based classification. It was designed to mitigate several sources of distortions often observed in PRPDs obtained from fully operational hydroelectric generators. The capabilities of the denoising algorithm are the automatic removal of sparse noise and the suppression of non-dominant discharges, including those due to crosstalk. The input features are functions of PD distributions along amplitude and phase, which are calculated in a novel way to mitigate random effects inherent to PD measurements. The impact of the proposed contributions was statistically evaluated and compared to classification performance obtained using formerly published approaches. Higher recognition rates and reduced variances were obtained using the proposed methods, statistically outperforming autonomous classification techniques seen in earlier works. The values of the algorithm’s internal parameters are also validated by comparing the recognition performance obtained with different parameter combinations. All typical PD sources described in hydro-generators PD standards are considered and can be automatically detected.

Keywords: partial discharges; PRPD denoising; PD recognition; condition monitoring; hydroelectric generators; histogram features (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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