Partial discharge classification using deep learning methods—survey of recent progress

Barrios, Sonia ; Buldain, David (Universidad de Zaragoza) ; Comech, María Paz (Universidad de Zaragoza) ; Gilbert, Ian ; Orue, Orue

Resumen: 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

Idioma: Inglés

DOI: 10.3390/en12132485

Año: 2019

Publicado en: Energies 12, 13 (2019), 2485 [16 pp.]
ISSN: 1996-1073

Factor impacto JCR: 2.702 (2019)

Categ. JCR: ENERGY & FUELS rank: 63 / 112 = 0.562 (2019) - Q3 - T2

Factor impacto SCIMAGO: 0.635 - Control and Optimization (Q2) - Electrical and Electronic Engineering (Q2) - Renewable Energy, Sustainability and the Environment (Q2) - Energy Engineering and Power Technology (Q2) - Fuel Technology (Q2) - Energy (miscellaneous) (Q2)

Financiación: info:eu-repo/grantAgreement/EC/H2020/676042/EU/Metrology Excellence Academic Network for Smart Grids/MEAN4SG

Tipo y forma: Revisión (Versión definitiva)

Área (Departamento): Área Tecnología Electrónica (Dpto. Ingeniería Electrón.Com.)
Área (Departamento): Área Ingeniería Eléctrica (Dpto. Ingeniería Eléctrica)