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

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 measu...

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Bibliographic Details
Published in:Energies (Basel) 2019-06, Vol.12 (13), p.2485
Main Authors: Barrios, Sonia, Buldain, David, Comech, María Paz, Gilbert, Ian, Orue, Iñaki
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Automation
Classification
Data storage
Deep learning
deep neural network
Fault diagnosis
fault recognition
Fourier transforms
International conferences
Machine learning
Neural networks
Neurons
Nodes
partial discharges
Pattern recognition
Sensors
Short term memory
Signal processing
Smart grid technology
Wavelet transforms
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Summary: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.
ISSN:1996-1073
1996-1073
DOI:10.3390/en12132485