Intelligent monitoring of transformer insulation using convolutional neural networks

Wei Lee Woon, Zeyar Aung, Ayman El-Hag

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

10 Scopus citations


The ability to monitor and detect potential faults in smart grid system components is extremely valuable. In this paper, we demonstrate the use of machine learning techniques for condition monitoring in power transformers. Our objective is to classify the three different types of Partial Discharge (PD), the identify of which is highly correlated with insulation failure. Measurements from Acoustic Emission (AE) sensors are used as input data. Two broad machine learning based approaches are considered - the conventional method which uses a predefined feature set (Fourier based), and deep learning where features are learned automatically from the data. The performance of deep learning compares very favorably to the traditional approach, which includes ensemble learning and support vector machines, while eliminating the need for explicit feature extraction from the input AE signals. The results are particularly encouraging as manual feature extraction is a subjective process that may require significant redesign when confronted with new operating conditions and data types. In contrast, the ability to automatically learn feature sets from the raw input data (AE signals) promises better generalization with minimal human intervention.

Original languageBritish English
Title of host publicationData Analytics for Renewable Energy Integration. Technologies, Systems and Society - 6th ECML PKDD Workshop, DARE 2018, Revised Selected Papers
EditorsAlejandro Catalina Feliú, Wei Lee Woon, Zeyar Aung, Stuart Madnick
PublisherSpringer Verlag
Number of pages10
ISBN (Print)9783030043025
StatePublished - 2018
Event6th International Workshop on Data Analytics for Renewable Energy Integration, DARE 2018 - Dublin, Ireland
Duration: 10 Sep 201810 Sep 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11325 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference6th International Workshop on Data Analytics for Renewable Energy Integration, DARE 2018


  • Convolutional neural networks
  • Deep learning
  • Machine learning
  • Partial discharge
  • Power transformer
  • Smart grid


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