Partial Discharge Classification Using Deep Learning Methods—Survey of Recent Progress

Sonia Barrios, David Buldain, María Paz Comech, Ian Gilbert and Iñaki Orue
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Sonia Barrios: Ormazabal Corporate Technology, 48340 Amorebieta, Spain
David Buldain: Department of Electronic Engineering and Communications, University of Zaragoza, 50018 Zaragoza, Spain
María Paz Comech: Instituto CIRCE (Universidad de Zaragoza—Fundación CIRCE), 50018 Zaragoza, Spain
Ian Gilbert: Ormazabal Corporate Technology, 48340 Amorebieta, Spain
Iñaki Orue: Ormazabal Corporate Technology, 48340 Amorebieta, Spain

Energies, 2019, vol. 12, issue 13, 1-16

Abstract: This paper examines the recent advances made in the field of Deep Learning (DL) methods for the automated identification of Partial Discharges (PD). PD activity is an indication of the state and operational conditions of electrical equipment systems. There are several techniques for on-line PD measurements, but the typical classification and recognition method is made off-line and involves an expert manually extracting appropriate features from raw data and then using these to diagnose PD type and severity. Many methods have been developed over the years, so that the appropriate features expertly extracted are used as input for Machine Learning (ML) algorithms. More recently, with the developments in computation and data storage, DL methods have been used for automated features extraction and classification. Several contributions have demonstrated that Deep Neural Networks (DNN) have better accuracy than the typical ML methods providing more efficient automated identification techniques. However, improvements could be made regarding the general applicability of the method, the data acquisition, and the optimal DNN structure.

Keywords: partial discharges; fault recognition; fault diagnosis; deep neural network; deep learning; machine learning (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: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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